------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  /Users/daniel.butler/Dropbox/My Mac (1064-AL-05001.lan)/Desktop/BHVW Results.log
  log type:  text
 opened on:   9 Sep 2021, 09:25:28

. do "/Users/daniel.butler/Dropbox/BHVW_Replication/Code/MTurkReplication.do"

. clear all

. set more off

. set scheme s1mono

. cd "~/Dropbox/BHVW_Replication/"
/Users/daniel.butler/Dropbox/BHVW_Replication

. * ssc install tabplot
. 
. *load the data
. use "Mturk/MTurkReplication.dta", clear
(Congress Survey 2015 (SID: 325664))

. cd "~/Dropbox/BHVW_Replication/Code/Results/"
/Users/daniel.butler/Dropbox/BHVW_Replication/Code/Results

. 
. *drop if Member not still in Congress
. drop if apiAH_2=="Allyson Schwartz"
(4 observations deleted)

. drop if apiAH_2=="Bill Cassidy"
(3 observations deleted)

. drop if apiAH_2=="Bill Enyart"
(0 observations deleted)

. drop if apiAH_2=="Bill Owens"
(2 observations deleted)

. drop if apiAH_2=="Bill Young"
(0 observations deleted)

. drop if apiAH_2=="Brad Schneider"
(3 observations deleted)

. drop if apiAH_2=="Bruce Braley"
(3 observations deleted)

. drop if apiAH_2=="Carol Shea-Porter"
(2 observations deleted)

. drop if apiAH_2=="Carolyn McCarthy"
(0 observations deleted)

. drop if apiAH_2=="Colleen Hanabusa"
(2 observations deleted)

. drop if apiAH_2=="Cory Gardner"
(1 observation deleted)

. drop if apiAH_2=="Dan Maffei"
(4 observations deleted)

. drop if apiAH_2=="Dave Camp"
(0 observations deleted)

. drop if apiAH_2=="Doc Hastings"
(0 observations deleted)

. drop if apiAH_2=="Donna Christensen"
(0 observations deleted)

. drop if apiAH_2=="Ed Pastor"
(3 observations deleted)

. drop if apiAH_2=="Edward Markey"
(0 observations deleted)

. drop if apiAH_2=="Eni Faleomavaega"
(0 observations deleted)

. drop if apiAH_2=="Eric Cantor"
(0 observations deleted)

. drop if apiAH_2=="Frank Wolf"
(2 observations deleted)

. drop if apiAH_2=="Gary Miller"
(4 observations deleted)

. drop if apiAH_2=="Gary Peters"
(0 observations deleted)

. drop if apiAH_2=="Gary Peters"
(0 observations deleted)

. drop if apiAH_2=="George Miller"
(2 observations deleted)

. drop if apiAH_2=="Gloria McLeod"
(0 observations deleted)

. drop if apiAH_2=="Henry Waxman"
(3 observations deleted)

. drop if apiAH_2=="Howard Buck"
(0 observations deleted)

. drop if apiAH_2=="Howard Coble"
(0 observations deleted)

. drop if apiAH_2=="Jack Kingston"
(4 observations deleted)

. drop if apiAH_2=="James Lankford"
(1 observation deleted)

. drop if apiAH_2=="James Lankford"
(0 observations deleted)

. drop if apiAH_2=="James Moran"
(0 observations deleted)

. drop if apiAH_2=="Jim Gerlach"
(3 observations deleted)

. drop if apiAH_2=="Jim Matheson"
(0 observations deleted)

. drop if apiAH_2=="Jo Bonner"
(0 observations deleted)

. drop if apiAH_2=="Jo Emerson"
(0 observations deleted)

. drop if apiAH_2=="Joe Garcia"
(4 observations deleted)

. drop if apiAH_2=="John Barrow"
(2 observations deleted)

. drop if apiAH_2=="John Campbell"
(0 observations deleted)

. drop if apiAH_2=="John Dingell"
(3 observations deleted)

. drop if apiAH_2=="John Tierney"
(0 observations deleted)

. drop if apiAH_2=="Jon Runyan"
(2 observations deleted)

. drop if apiAH_2=="Kerry Bentivolio"
(2 observations deleted)

. drop if apiAH_2=="Lee Terry"
(3 observations deleted)

. drop if apiAH_2=="Melvin Watt"
(0 observations deleted)

. drop if apiAH_2=="Michael Grimm."
(0 observations deleted)

. drop if apiAH_2=="Michael Michaud"
(0 observations deleted)

. drop if apiAH_2=="Michele Bachmann"
(0 observations deleted)

. drop if apiAH_2=="Mike McIntyre"
(2 observations deleted)

. drop if apiAH_2=="Mike Rogers"
(0 observations deleted)

. drop if apiAH_2=="Nick Rahall"
(1 observation deleted)

. drop if apiAH_2=="Paul Broun"
(1 observation deleted)

. drop if apiAH_2=="Pete Gallego"
(0 observations deleted)

. drop if apiAH_2=="Phil Gingrey"
(7 observations deleted)

. drop if apiAH_2=="Ralph Hall"
(1 observation deleted)

. drop if apiAH_2=="Robert Andrews"
(0 observations deleted)

. drop if apiAH_2=="Rodney Alexander"
(0 observations deleted)

. drop if apiAH_2=="Ron Barber"
(2 observations deleted)

. drop if apiAH_2=="Rush Holt"
(0 observations deleted)

. drop if apiAH_2=="Shelley Moore"
(0 observations deleted)

. drop if apiAH_2=="Shelley Moore"
(0 observations deleted)

. drop if apiAH_2=="Spencer Bachus"
(3 observations deleted)

. drop if apiAH_2=="Steve Daines"
(3 observations deleted)

. drop if apiAH_2=="Steve Daines"
(0 observations deleted)

. drop if apiAH_2=="Steve Southerland"
(5 observations deleted)

. drop if apiAH_2=="Steve Stockman"
(3 observations deleted)

. drop if apiAH_2=="Steven Horsford"
(6 observations deleted)

. drop if apiAH_2=="Tim Griffin"
(0 observations deleted)

. drop if apiAH_2=="Tim Scott"
(0 observations deleted)

. drop if apiAH_2=="Timothy Bishop"
(0 observations deleted)

. drop if apiAH_2=="Tom Cotton"
(0 observations deleted)

. drop if apiAH_2=="Tom Cotton"
(0 observations deleted)

. drop if apiAH_2=="Tom Latham"
(3 observations deleted)

. drop if apiAH_2=="Tom Petri"
(0 observations deleted)

. drop if apiAH_2=="Trey Radel"
(0 observations deleted)

. drop if apiAH_2=="Vance McAllister"
(3 observations deleted)

. drop if apiAH_2==""
(41 observations deleted)

. drop if apiAH_2=="Aaron Schock"
(1 observation deleted)

. drop if apiAH_2=="John Boehner"
(4 observations deleted)

. drop if apiAH_2=="Michael Grimm"
(5 observations deleted)

. drop if apiAH_2=="Alan Nunelee"
(3 observations deleted)

. 
. *CODE META VARIABLES
. *create a dummy for effectiveness information treatment (called efinfo)
. gen efinfo=0

. replace efinfo=1 if rand13==3
(276 real changes made)

. replace efinfo=1 if rand13==4
(260 real changes made)

. label var efinfo "1 = Treatment, received effetiveness information"

. 
. *create indicator variables for R's representative's true effectiveness
. gen effective=0

. replace effective=1 if apiAH_5=="Highly Effective"
(237 real changes made)

. label var effective "1 = Actually highly effective"

. gen ineffective=0

. replace ineffective=1 if apiAH_5=="Not Effective"
(421 real changes made)

. label var ineffective "1 = Actually ineffective"

. gen avgeffective=0

. replace avgeffective=1 if apiAH_5=="Average in Effectiveness"
(376 real changes made)

. label var avgeffective "1 = Actually average in effectiveness"

. 
. *code r's subjective effectiveness evaluations (POST TREATMENT)
. gen r_effective=0

. replace r_effective=1 if lesrecall==1 
(74 real changes made)

. label var r_effective "1 = Perceived highly effective"

. gen r_ineffective=0

. replace r_ineffective=1 if lesrecall==3
(224 real changes made)

. label var r_ineffective "1 = Perceived ineffective"

. gen r_avgeffective=0

. replace r_avgeffective=1 if lesrecall==2
(723 real changes made)

. label var r_avgeffective "1 = Perceived average in effectiveness"

. 
. *CODE KEY EXPLANATORY VARIABLES
. *Interaction Version
. gen effinteract=effective*efinfo

. label var effinteract "1 = Treatment and actually highly effective"

. gen ineffinteract=ineffective*efinfo

. label var ineffinteract "1 = Treatment and actually ineffective"

. 
. *CODE RESPONDENT DEMOGRAPHICS
. *code ideology
. gen lib=0

. replace lib=1 if ideology==1 | ideology==2 | ideology==3
(565 real changes made)

. label var lib "1 = anywhere on liberal end of scale, non-moderate"

. gen con=0

. replace con=1 if ideology==5 | ideology==6 | ideology==7
(244 real changes made)

. label var con "1 = anywhere on conservative end of scale, non-moderate"

. 
. *code partisanship
. gen rep=0

. replace rep=1 if anes_pid=="1"
(186 real changes made)

.         *include leaners
. replace rep=1 if anes_pidi==1
(130 real changes made)

. label var rep "1 = Republican, including leaners"

. gen dem=0

. replace dem=1 if anes_pid=="2"
(484 real changes made)

.         *include leaners
. replace dem=1 if anes_pidi==2
(222 real changes made)

. label var dem "1 = Democrat, including leaners"

. 
. gen copartisan=.
(1,035 missing values generated)

. replace copartisan=1 if dem==1 &apiAH_3=="Democrat"
(379 real changes made)

. replace copartisan=1 if rep==1 &apiAH_3=="Republican"
(200 real changes made)

. replace copartisan=0 if rep==1 &apiAH_3=="Democrat"
(115 real changes made)

. replace copartisan=0 if dem==1 &apiAH_3=="Republican"
(327 real changes made)

. label var copartisan "1 = Respondent and Representative in same party"

. 
. *code rep party for each respondent
. gen rdem=0 

. replace rdem=1 if apiAH_3=="Democrat"
(502 real changes made)

. label var rdem "1 = Representative is a Democrat"

. gen rrep=0  

. replace rrep=1 if apiAH_3=="Republican"
(532 real changes made)

. label var rrep "1 = Representative is a Republican"

. 
. gen trueeff=.
(1,035 missing values generated)

. replace trueeff=0 if ineffective==1
(421 real changes made)

. replace trueeff=1 if avgeffective==1
(376 real changes made)

. replace trueeff=2 if effective==1
(237 real changes made)

. label var trueeff "0 = actual ineffective, 1 = actual average, 2 = actual highly effective"

. 
.         gen true_eff2=3-trueeff /*Do this to ensure that we actually have it in same scale as perceived ef
> fectiveness (lesrecall)*/
(1 missing value generated)

. 
. *CREATE KEY DEPENDENT VARIABLES
. gen voteforrep=0

. replace voteforrep=1 if voteintent==1
(78 real changes made)

. replace voteforrep=1 if voteintent==2
(379 real changes made)

. label var voteforrep "1 = intend to vote for incumbent"

. 
. gen approve=0

. replace approve=1 if approvedisapprove==1
(38 real changes made)

. replace approve=1 if approvedisapprove==2
(530 real changes made)

. replace approve=0 if approvedisapprove==3
(0 real changes made)

. replace approve=0 if approvedisapprove==4
(0 real changes made)

. label var approve "1 = Approve or strongly approve job of incumbent"

. 
. *4 category approval
. gen approve4=0

. replace approve4=0 if approvedisapprove==4
(0 real changes made)

. replace approve4=.333 if approvedisapprove==3
(359 real changes made)

. replace approve4=.666 if approvedisapprove==2
(530 real changes made)

. replace approve4=1 if approvedisapprove==1
(38 real changes made)

. label var approve4 "Four category job approval"

. 
. /*
> *check balance
> ttest effective,by(efinfo)
> ttest avgeffective,by(efinfo)
> ttest ineffective,by(efinfo)
> ttest dem,by(efinfo)
> ttest lib,by(efinfo)
> ttest rep,by(efinfo)
> ttest con,by(efinfo)
> ttest age,by(efinfo)
> ttest education,by(efinfo)
> ttest income,by(efinfo)
> ttest gender,by(efinfo)
> 
> egen thermevals01 = std01(thermevals_1)
> gen voteforrep01=.
> replace voteforrep01=1 if voteintent==1
> replace voteforrep01=.75 if voteintent==2
> replace voteforrep01=.25 if voteintent==3
> replace voteforrep01=0 if voteintent==4
> */
. 
. 
. *********************
. *********************
. ******ANALYSIS*******
. *********************
. *********************
. 
. ** Outcome = Approve of MC
. 
. ** Basic Levels of Knowledge in Control Group (For Figure 1 in Paper)
.         tab lesrecall true_eff2 if efinfo==0, chi

 How effective do you |
  think {apiAH_2} is, |
  in terms of passing |            true_eff2
              laws?     � |         1          2          3 |     Total
----------------------+---------------------------------+----------
     Highly effective |         3          9         10 |        22 
About average in effe |        85        159        136 |       380 
 Not at all effective |        19         24         45 |        88 
----------------------+---------------------------------+----------
                Total |       107        192        191 |       490 

          Pearson chi2(4) =   9.1826   Pr = 0.057

.         corr  lesrecall true_eff2 if efinfo==0
(obs=490)

             | lesrec~l true_e~2
-------------+------------------
   lesrecall |   1.0000
   true_eff2 |   0.0454   1.0000


.         tabplot lesrecall trueeff if efinfo==0, percent(trueeff)  horizontal ytitle("Perceived" "Effective
> ness") ylab(1 "Ineffective" 2 "Average" 3 "Highly Effective") xtitle( " " "Actual Effectiveness") xlabel(1
>  "Ineffective" 2 "Average" 3 "Highly Effective") showval(format(%2.1f) mlabsize(vsmall) offset(0.25)) titl
> e("MTurk Survey (Control Group)", size(medium)) subtitle(" ")

.                 graph save knowledge_mturk.gph, replace 
(file knowledge_mturk.gph not found)
file knowledge_mturk.gph saved

.         
. 
. ** Relationship between approval and actual/perceived Effectiveness in Control Group (for Figure 2 in Pape
> r)
.         bysort trueeff: egen perc_approve_true=mean(approve) if efinfo==0
(536 missing values generated)

.         bysort lesrecall: egen perc_approve_perceived=mean(approve) if efinfo==0
(536 missing values generated)

.         graph bar (mean) perc_approve_true, ylabel(0(.2)1) over(trueeff, relabel(1"Ineffective" 2 "Average
> " 3 "Highly Effective")) ytitle(Percent Approving of Legislator) title("MTurk, Actual Effectiveness") 

.                 graph save approve_actual_mturk.gph, replace 
(file approve_actual_mturk.gph not found)
file approve_actual_mturk.gph saved

.                 
.                 gen l2=4-lesrecall /*Create this in order to get scale right on graph*/
(14 missing values generated)

.         graph bar (mean) perc_approve_perceived if efinfo==0, ylabel(0(.2)1) over(l2, relabel(1"Ineffectiv
> e" 2 "Average" 3 "Highly Effective")) ytitle(Percent Approving of Legislator) title("MTurk, Perceived Effe
> ctiveness")

.                 graph save approve_perceived_mturk.gph, replace 
(file approve_perceived_mturk.gph not found)
file approve_perceived_mturk.gph saved

. 
. *************************************
. ** Figure 4: Main Treatment Effect **
. *************************************
. 
. ** Results in Paper
.         reg approve efinfo if effective==1

      Source |       SS           df       MS      Number of obs   =       237
-------------+----------------------------------   F(1, 235)       =     11.40
       Model |  2.57055156         1  2.57055156   Prob > F        =    0.0009
    Residual |  53.0075075       235  .225563862   R-squared       =    0.0463
-------------+----------------------------------   Adj R-squared   =    0.0422
       Total |  55.5780591       236   .23550025   Root MSE        =    .47494

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |   .2087087   .0618247     3.38   0.001     .0869072    .3305102
       _cons |   .5135135   .0450789    11.39   0.000     .4247031    .6023239
------------------------------------------------------------------------------

.         est store Effective

. 
.         reg approve efinfo if avgeffective==1

      Source |       SS           df       MS      Number of obs   =       376
-------------+----------------------------------   F(1, 374)       =      0.14
       Model |  .032823647         1  .032823647   Prob > F        =    0.7117
    Residual |  89.7118572       374  .239871276   R-squared       =    0.0004
-------------+----------------------------------   Adj R-squared   =   -0.0023
       Total |  89.7446809       375  .239319149   Root MSE        =    .48977

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |  -.0186995   .0505506    -0.37   0.712    -.1180986    .0806995
       _cons |   .6153846   .0350729    17.55   0.000     .5464198    .6843494
------------------------------------------------------------------------------

.         est store Avg_Effective

. 
.         reg approve efinfo if ineffective==1 

      Source |       SS           df       MS      Number of obs   =       421
-------------+----------------------------------   F(1, 419)       =     31.07
       Model |  7.20344274         1  7.20344274   Prob > F        =    0.0000
    Residual |  97.1433506       419  .231845705   R-squared       =    0.0690
-------------+----------------------------------   Adj R-squared   =    0.0668
       Total |  104.346793       420  .248444746   Root MSE        =     .4815

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |  -.2625216   .0470971    -5.57   0.000    -.3550977   -.1699455
       _cons |   .5958549   .0346594    17.19   0.000      .527727    .6639829
------------------------------------------------------------------------------

.         est store Ineffective

.         #delimit;
delimiter now ;
.         coefplot (Effective, drop(_cons) mcol(green) label(Highly Effective)) (Avg_Effective, drop(_cons) 
> mcol(black) label(Average)) 
>         (Ineffective, drop(_cons) mcol(red)),  ylabel("") title("MTurk, Approval of Lawmaker, Treatment Ef
> fects", size(medium)) 
>          legend(row(1) order(6 4 2)) 
>          xlabel (-.4 "-.4" -.2 "-.2" 0 "0" .2 ".2" .4 ".4" -.3 `"" " "Lower Approval""'  .3`"" "  "Higher 
> Approval""', noticks)
>         xtitle(Effect of Effectiveness Information on Approval of their Lawmaker);

.          #delimit cr
delimiter now cr
.                 graph save "MainTreatment_Mturk.gph", replace
(file MainTreatment_Mturk.gph not found)
file MainTreatment_Mturk.gph saved

.                 
. ** Appendix
.         logit approve efinfo if effective==1

Iteration 0:   log likelihood =  -156.8542  
Iteration 1:   log likelihood = -151.36124  
Iteration 2:   log likelihood = -151.34492  
Iteration 3:   log likelihood = -151.34491  

Logistic regression                                     Number of obs =    237
                                                        LR chi2(1)    =  11.02
                                                        Prob > chi2   = 0.0009
Log likelihood = -151.34491                             Pseudo R2     = 0.0351

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |   .9014442    .274996     3.28   0.001     .3624619    1.440427
       _cons |   .0540672    .189901     0.28   0.776    -.3181318    .4262663
------------------------------------------------------------------------------

.         margins, at(efinfo=(0(1)1)) post

Adjusted predictions                                       Number of obs = 237
Model VCE: OIM

Expression: Pr(approve), predict()
1._at: efinfo = 0
2._at: efinfo = 1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .5135135   .0474406    10.82   0.000     .4205317    .6064953
          2  |   .7222222   .0399024    18.10   0.000      .644015    .8004295
------------------------------------------------------------------------------

.         est store Effective

. 
.         logit approve efinfo if avgeffective==1

Iteration 0:   log likelihood =  -252.0473  
Iteration 1:   log likelihood = -251.97855  
Iteration 2:   log likelihood = -251.97855  

Logistic regression                                     Number of obs =    376
                                                        LR chi2(1)    =   0.14
                                                        Prob > chi2   = 0.7108
Log likelihood = -251.97855                             Pseudo R2     = 0.0003

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |  -.0783318   .2112453    -0.37   0.711    -.4923649    .3357012
       _cons |   .4700036    .147196     3.19   0.001     .1815047    .7585025
------------------------------------------------------------------------------

.         margins, at(efinfo=(0(1)1)) post

Adjusted predictions                                       Number of obs = 376
Model VCE: OIM

Expression: Pr(approve), predict()
1._at: efinfo = 0
2._at: efinfo = 1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .6153846   .0348393    17.66   0.000     .5471009    .6836684
          2  |   .5966851   .0364633    16.36   0.000     .5252184    .6681518
------------------------------------------------------------------------------

.         est store Avg_Effective

. 
.         logit approve efinfo if ineffective==1 

Iteration 0:   log likelihood = -290.00596  
Iteration 1:   log likelihood =   -275.346  
Iteration 2:   log likelihood = -275.33395  
Iteration 3:   log likelihood = -275.33395  

Logistic regression                                     Number of obs =    421
                                                        LR chi2(1)    =  29.34
                                                        Prob > chi2   = 0.0000
Log likelihood = -275.33395                             Pseudo R2     = 0.0506

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |   -1.08137   .2031084    -5.32   0.000    -1.479456   -.6832854
       _cons |   .3882233   .1466839     2.65   0.008     .1007282    .6757185
------------------------------------------------------------------------------

.         margins, at(efinfo=(0(1)1)) post

Adjusted predictions                                       Number of obs = 421
Model VCE: OIM

Expression: Pr(approve), predict()
1._at: efinfo = 0
2._at: efinfo = 1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .5958549   .0353232    16.87   0.000     .5266227    .6650872
          2  |   .3333333   .0312195    10.68   0.000     .2721442    .3945225
------------------------------------------------------------------------------

.         est store Ineffective

. 
.         coefplot (Effective, mcol(green) label(Highly Effective)) (Avg_Effective, mcol(black) label(Averag
> e)) (Ineffective, mcol(red)),  ylabel(`=1' "Control" `=2' "Treated") title("MTurk, Approval of Lawmaker", 
> size(medium))       legend(row(1) order(6 4 2))     

.                 graph save "MainTreatment_Mturk_Appendix.gph", replace
(file MainTreatment_Mturk_Appendix.gph not found)
file MainTreatment_Mturk_Appendix.gph saved

. 
. 
. ******************************************************************
. ** Figure 5: Heterogeneous Treatment Effects, by copartisanship **
. ******************************************************************
. 
. ** Results in Paper
.         reg approve efinfo if effective==1 & copartisan==1

      Source |       SS           df       MS      Number of obs   =       125
-------------+----------------------------------   F(1, 123)       =      3.69
       Model |  .468807512         1  .468807512   Prob > F        =    0.0572
    Residual |  15.6431925       123  .127180427   R-squared       =    0.0291
-------------+----------------------------------   Adj R-squared   =    0.0212
       Total |      16.112       124  .129935484   Root MSE        =    .35662

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |   .1236307   .0643931     1.92   0.057    -.0038314    .2510928
       _cons |   .7777778   .0485303    16.03   0.000      .681715    .8738406
------------------------------------------------------------------------------

.                 est store E1

.         reg approve efinfo if effective==1 & copartisan==0

      Source |       SS           df       MS      Number of obs   =       108
-------------+----------------------------------   F(1, 106)       =      5.62
       Model |  1.28081126         1  1.28081126   Prob > F        =    0.0196
    Residual |  24.1543739       106  .227871452   R-squared       =    0.0504
-------------+----------------------------------   Adj R-squared   =    0.0414
       Total |  25.4351852       107  .237712011   Root MSE        =    .47736

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |   .2178388   .0918835     2.37   0.020     .0356708    .4000068
       _cons |   .2727273   .0643671     4.24   0.000     .1451133    .4003412
------------------------------------------------------------------------------

.                 est store E2

.                 
.         reg approve efinfo if avgeffective==1 & copartisan==1

      Source |       SS           df       MS      Number of obs   =       196
-------------+----------------------------------   F(1, 194)       =      0.50
       Model |  .067355442         1  .067355442   Prob > F        =    0.4795
    Residual |  26.0295833       194   .13417311   R-squared       =    0.0026
-------------+----------------------------------   Adj R-squared   =   -0.0026
       Total |  26.0969388       195  .133830455   Root MSE        =     .3663

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |  -.0370833    .052339    -0.71   0.479    -.1403098    .0661431
       _cons |        .86   .0366296    23.48   0.000     .7877565    .9322435
------------------------------------------------------------------------------

.                 est store A1

.         reg approve efinfo if avgeffective==1 & copartisan==0

      Source |       SS           df       MS      Number of obs   =       174
-------------+----------------------------------   F(1, 172)       =      0.38
       Model |  .086131367         1  .086131367   Prob > F        =    0.5397
    Residual |  39.2242135       172  .228047753   R-squared       =    0.0022
-------------+----------------------------------   Adj R-squared   =   -0.0036
       Total |  39.3103448       173  .227227427   Root MSE        =    .47754

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |  -.0446037   .0725778    -0.61   0.540    -.1878615     .098654
       _cons |   .3655914   .0495189     7.38   0.000     .2678483    .4633345
------------------------------------------------------------------------------

.                 est store A2

. 
.         reg approve efinfo if ineffective==1 & copartisan==1

      Source |       SS           df       MS      Number of obs   =       258
-------------+----------------------------------   F(1, 256)       =     38.49
       Model |  8.11345916         1  8.11345916   Prob > F        =    0.0000
    Residual |  53.9640602       256   .21079711   R-squared       =    0.1307
-------------+----------------------------------   Adj R-squared   =    0.1273
       Total |  62.0775194       257  .241546768   Root MSE        =    .45913

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |  -.3564837   .0574604    -6.20   0.000    -.4696391   -.2433284
       _cons |   .7931034   .0426288    18.60   0.000     .7091556    .8770513
------------------------------------------------------------------------------

.                 est store I1

.         reg approve efinfo if ineffective==1 & copartisan==0

      Source |       SS           df       MS      Number of obs   =       160
-------------+----------------------------------   F(1, 158)       =      3.82
       Model |  .659215686         1  .659215686   Prob > F        =    0.0523
    Residual |  27.2407843       158  .172410027   R-squared       =    0.0236
-------------+----------------------------------   Adj R-squared   =    0.0174
       Total |        27.9       159  .175471698   Root MSE        =    .41522

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |  -.1286275   .0657811    -1.96   0.052    -.2585512    .0012963
       _cons |   .2933333   .0479458     6.12   0.000      .198636    .3880307
------------------------------------------------------------------------------

.                 est store I2

. 
.         coefplot (E1, drop(_cons) mcol(green) msymbol(O) label(Highly Effective)) (A1, drop(_cons) mcol(bl
> ack) msymbol(D)  label(Average)) (I1, drop(_cons) mcol(red) msymbol(S)  label(Ineffective))   (E2, drop(_c
> ons) mcol(green) msymbol(O) label(Highly Effective)) (A2, drop(_cons) mcol(black) msymbol(D) label(Average
> )) (I2, drop(_cons) mcol(red) msymbol(S) label(Ineffective)),  ylabel("")  title("MTurk Approval, by Parti
> san Alignment")  ylabel(.8 "Copartisan" 1.2 "Outparty") legend(row(1) order(6 4 2)) xlabel (-.4 "-.4" -.2 
> "-.2" 0 "0" .2 ".2" .4 ".4" -.3 `"" " "Lower Approval""'  .3`"" "  "Higher Approval""', noticks) xtitle(Ef
> fect of Effectiveness Information on Approval of their Lawmaker)

.                 graph save "Partisanship_MTurk.gph", replace
(file Partisanship_MTurk.gph not found)
file Partisanship_MTurk.gph saved

.         
. ** Appendix
.         logit approve copartisan##efinfo if effective==1 

Iteration 0:   log likelihood =  -153.4245  
Iteration 1:   log likelihood = -121.20867  
Iteration 2:   log likelihood = -120.42715  
Iteration 3:   log likelihood = -120.41928  
Iteration 4:   log likelihood = -120.41928  

Logistic regression                                     Number of obs =    233
                                                        LR chi2(3)    =  66.01
                                                        Prob > chi2   = 0.0000
Log likelihood = -120.41928                             Pseudo R2     = 0.2151

-----------------------------------------------------------------------------------
          approve | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
------------------+----------------------------------------------------------------
     1.copartisan |   2.233592   .4458806     5.01   0.000     1.359682    3.107502
         1.efinfo |   .9430889   .4088585     2.31   0.021     .1417411    1.744437
                  |
copartisan#efinfo |
             1 1  |   .0171207   .6578679     0.03   0.979    -1.272277    1.306518
                  |
            _cons |  -.9808293    .302765    -3.24   0.001    -1.574238   -.3874207
-----------------------------------------------------------------------------------

.                 margins, at(efinfo=(0(1)1) copartisan=(0(1)1)) post

Adjusted predictions                                       Number of obs = 233
Model VCE: OIM

Expression: Pr(approve), predict()
1._at: copartisan = 0
       efinfo     = 0
2._at: copartisan = 0
       efinfo     = 1
3._at: copartisan = 1
       efinfo     = 0
4._at: copartisan = 1
       efinfo     = 1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .2727273   .0600526     4.54   0.000     .1550264    .3904281
          2  |    .490566   .0686681     7.14   0.000     .3559791     .625153
          3  |   .7777778    .056575    13.75   0.000     .6668928    .8886628
          4  |   .9014084   .0353795    25.48   0.000     .8320659    .9707509
------------------------------------------------------------------------------

.                 est store Effective

.         
.         logit approve copartisan##efinfo if avgeffective==1

Iteration 0:   log likelihood = -247.74713  
Iteration 1:   log likelihood = -197.98222  
Iteration 2:   log likelihood = -197.22138  
Iteration 3:   log likelihood =  -197.2201  
Iteration 4:   log likelihood =  -197.2201  

Logistic regression                                     Number of obs =    370
                                                        LR chi2(3)    = 101.05
                                                        Prob > chi2   = 0.0000
Log likelihood = -197.2201                              Pseudo R2     = 0.2039

-----------------------------------------------------------------------------------
          approve | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
------------------+----------------------------------------------------------------
     1.copartisan |   2.366467   .3597463     6.58   0.000     1.661377    3.071557
         1.efinfo |  -.1980597   .3209428    -0.62   0.537     -.827096    .4309766
                  |
copartisan#efinfo |
             1 1  |  -.0809957   .5074865    -0.16   0.873    -1.075651    .9136595
                  |
            _cons |  -.5511769   .2153159    -2.56   0.010    -.9731882   -.1291656
-----------------------------------------------------------------------------------

.                 margins, at(efinfo=(0(1)1) copartisan=(0(1)1)) post

Adjusted predictions                                       Number of obs = 370
Model VCE: OIM

Expression: Pr(approve), predict()
1._at: copartisan = 0
       efinfo     = 0
2._at: copartisan = 0
       efinfo     = 1
3._at: copartisan = 1
       efinfo     = 0
4._at: copartisan = 1
       efinfo     = 1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .3655914   .0499391     7.32   0.000     .2677125    .4634703
          2  |   .3209877   .0518729     6.19   0.000     .2193186    .4226567
          3  |        .86   .0346987    24.78   0.000     .7919918    .9280082
          4  |   .8229167   .0389611    21.12   0.000     .7465543     .899279
------------------------------------------------------------------------------

.                 est store Avg_Effective

. 
.         logit approve copartisan##efinfo if ineffective==1 

Iteration 0:   log likelihood = -288.00586  
Iteration 1:   log likelihood = -239.94035  
Iteration 2:   log likelihood = -239.83323  
Iteration 3:   log likelihood = -239.83309  
Iteration 4:   log likelihood = -239.83309  

Logistic regression                                     Number of obs =    418
                                                        LR chi2(3)    =  96.35
                                                        Prob > chi2   = 0.0000
Log likelihood = -239.83309                             Pseudo R2     = 0.1673

-----------------------------------------------------------------------------------
          approve | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
------------------+----------------------------------------------------------------
     1.copartisan |   2.222984    .341846     6.50   0.000     1.552978     2.89299
         1.efinfo |  -.7443731    .387086    -1.92   0.054    -1.503048    .0143015
                  |
copartisan#efinfo |
             1 1  |  -.8542539   .4806254    -1.78   0.076    -1.796262    .0877546
                  |
            _cons |  -.8792495   .2536187    -3.47   0.001    -1.376333   -.3821658
-----------------------------------------------------------------------------------

.                 margins, at(efinfo=(0(1)1) copartisan=(0(1)1)) post

Adjusted predictions                                       Number of obs = 418
Model VCE: OIM

Expression: Pr(approve), predict()
1._at: copartisan = 0
       efinfo     = 0
2._at: copartisan = 0
       efinfo     = 1
3._at: copartisan = 1
       efinfo     = 0
4._at: copartisan = 1
       efinfo     = 1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .2933333   .0525723     5.58   0.000     .1902934    .3963732
          2  |   .1647059   .0402314     4.09   0.000     .0858538    .2435579
          3  |   .7931034   .0376108    21.09   0.000     .7193876    .8668193
          4  |   .4366197   .0416206    10.49   0.000     .3550448    .5181946
------------------------------------------------------------------------------

.                 est store Ineffective

. 
.         coefplot (Effective, mcol(green) label(Highly Effective)) (Avg_Effective, mcol(black) label(Averag
> e)) (Ineffective, mcol(red)),  ylabel(`=1' "Outparty, Control" `=2' "Outparty, Treated" `=3' "Copartisan, 
> Control" `=4' "Copartisan, Treated")  title("MTurk Approval, by Partisan Alignment") legend(row(1) order(6
>  4 2))

.                 graph save "Partisanship_MTurk_Appendix.gph", replace
(file Partisanship_MTurk_Appendix.gph not found)
file Partisanship_MTurk_Appendix.gph saved

. 
.         
. ************************************************************
. ** Figure 6: Heterogenenous treatment effects by ideology **
. ************************************************************
. 
. *Create ideological extremism indicator
. gen extreme=0

. replace extreme=1 if ideology==1 | ideology==2 | ideology==6 | ideology==7
(555 real changes made)

. label var extreme "1 = Self identified as Very Liberal, Liberal, Conservative, or Very Conservative"

. 
. ** Results in Paper
.         reg approve efinfo if effective==1 & extreme==1

      Source |       SS           df       MS      Number of obs   =       128
-------------+----------------------------------   F(1, 126)       =      3.86
       Model |    .9453125         1    .9453125   Prob > F        =    0.0517
    Residual |   30.859375       126  .244915675   R-squared       =    0.0297
-------------+----------------------------------   Adj R-squared   =    0.0220
       Total |  31.8046875       127   .25043061   Root MSE        =    .49489

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |    .171875   .0874849     1.96   0.052    -.0012551    .3450051
       _cons |    .453125   .0618612     7.32   0.000     .3307035    .5755465
------------------------------------------------------------------------------

.                 est store E1

.         reg approve efinfo if effective==1 & extreme==0

      Source |       SS           df       MS      Number of obs   =       109
-------------+----------------------------------   F(1, 107)       =      7.23
       Model |  1.37558323         1  1.37558323   Prob > F        =    0.0083
    Residual |   20.367536       107  .190350804   R-squared       =    0.0633
-------------+----------------------------------   Adj R-squared   =    0.0545
       Total |  21.7431193       108  .201325178   Root MSE        =    .43629

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |    .226836   .0843812     2.69   0.008       .05956    .3941119
       _cons |   .5957447   .0636397     9.36   0.000     .4695863    .7219031
------------------------------------------------------------------------------

.                 est store E2

.                 
.         reg approve efinfo if avgeffective==1 & extreme==1

      Source |       SS           df       MS      Number of obs   =       193
-------------+----------------------------------   F(1, 191)       =      0.54
       Model |   .12935217         1   .12935217   Prob > F        =    0.4653
    Residual |  46.1504406       191  .241625343   R-squared       =    0.0028
-------------+----------------------------------   Adj R-squared   =   -0.0024
       Total |  46.2797927       192  .241040587   Root MSE        =    .49155

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |  -.0517945   .0707894    -0.73   0.465     -.191424    .0878349
       _cons |   .6262626    .049403    12.68   0.000      .528817    .7237082
------------------------------------------------------------------------------

.                 est store A1

.         reg approve efinfo if avgeffective==1 & extreme==0

      Source |       SS           df       MS      Number of obs   =       183
-------------+----------------------------------   F(1, 181)       =      0.05
       Model |  .012459959         1  .012459959   Prob > F        =    0.8200
    Residual |   43.441092       181  .240006033   R-squared       =    0.0003
-------------+----------------------------------   Adj R-squared   =   -0.0052
       Total |  43.4535519       182   .23875578   Root MSE        =     .4899

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |    .016523   .0725173     0.23   0.820     -.126565    .1596109
       _cons |   .6041667   .0500006    12.08   0.000     .5055076    .7028258
------------------------------------------------------------------------------

.                 est store A2

. 
.         reg approve efinfo if ineffective==1 & extreme==1

      Source |       SS           df       MS      Number of obs   =       234
-------------+----------------------------------   F(1, 232)       =      9.91
       Model |  2.37900203         1  2.37900203   Prob > F        =    0.0019
    Residual |  55.6936475       232  .240058826   R-squared       =    0.0410
-------------+----------------------------------   Adj R-squared   =    0.0368
       Total |  58.0726496       233  .249238839   Root MSE        =    .48996

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |  -.2018443   .0641177    -3.15   0.002    -.3281716   -.0755169
       _cons |      .5625   .0462967    12.15   0.000     .4712843    .6537157
------------------------------------------------------------------------------

.                 est store I1

.         reg approve efinfo if ineffective==1 & extreme==0

      Source |       SS           df       MS      Number of obs   =       187
-------------+----------------------------------   F(1, 185)       =     23.99
       Model |  5.31047309         1  5.31047309   Prob > F        =    0.0000
    Residual |  40.9569066       185  .221388684   R-squared       =    0.1148
-------------+----------------------------------   Adj R-squared   =    0.1100
       Total |  46.2673797       186  .248749353   Root MSE        =    .47052

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |  -.3400885   .0694389    -4.90   0.000    -.4770825   -.2030945
       _cons |   .6419753     .05228    12.28   0.000     .5388338    .7451169
------------------------------------------------------------------------------

.                 est store I2

. 
.         coefplot (E1, drop(_cons) mcol(green) msymbol(O) label(Highly Effective)) (A1, drop(_cons) mcol(bl
> ack) msymbol(D)  label(Average)) (I1, drop(_cons) mcol(red) msymbol(S)  label(Ineffective))   (E2, drop(_c
> ons) mcol(green) msymbol(O) label(Highly Effective)) (A2, drop(_cons) mcol(black) msymbol(D) label(Average
> )) (I2, drop(_cons) mcol(red) msymbol(S) label(Ineffective)),  ylabel("")  title("MTurk Approval, by Ideol
> ogical Extermism") legend(row(1) order(6 4 2)) ylabel(.8 "Extreme" 1.2 "Moderate") xlabel (-.4 "-.4" -.2 "
> -.2" 0 "0" .2 ".2" .4 ".4" -.3 `"" " "Lower Approval""'  .3`"" "  "Higher Approval""', noticks) xtitle(Eff
> ect of Effectiveness Information on Approval of their Lawmaker)

.                 graph save "IdeologicalExtremism_MTurk.gph", replace
(file IdeologicalExtremism_MTurk.gph not found)
file IdeologicalExtremism_MTurk.gph saved

. 
. ** Appendix
. logit approve extreme##efinfo if effective==1 

Iteration 0:   log likelihood =  -156.8542  
Iteration 1:   log likelihood = -147.26461  
Iteration 2:   log likelihood = -147.11307  
Iteration 3:   log likelihood = -147.11305  
Iteration 4:   log likelihood = -147.11305  

Logistic regression                                     Number of obs =    237
                                                        LR chi2(3)    =  19.48
                                                        Prob > chi2   = 0.0002
Log likelihood = -147.11305                             Pseudo R2     = 0.0621

--------------------------------------------------------------------------------
       approve | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
---------------+----------------------------------------------------------------
     1.extreme |  -.5758178   .3891016    -1.48   0.139    -1.338443    .1868074
      1.efinfo |   1.146165   .4459404     2.57   0.010     .2721378    2.020192
               |
extreme#efinfo |
          1 1  |   -.447287   .5732222    -0.78   0.435    -1.570782    .6762078
               |
         _cons |   .3877655   .2972303     1.30   0.192    -.1947952    .9703263
--------------------------------------------------------------------------------

.                 margins, at(efinfo=(0(1)1) extreme=(0(1)1)) post

Adjusted predictions                                       Number of obs = 237
Model VCE: OIM

Expression: Pr(approve), predict()
1._at: extreme = 0
       efinfo  = 0
2._at: extreme = 0
       efinfo  = 1
3._at: extreme = 1
       efinfo  = 0
4._at: extreme = 1
       efinfo  = 1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .5957447   .0715829     8.32   0.000     .4554449    .7360445
          2  |   .8225806    .048517    16.95   0.000     .7274891    .9176722
          3  |    .453125   .0622247     7.28   0.000     .3311668    .5750832
          4  |       .625   .0605154    10.33   0.000     .5063921    .7436079
------------------------------------------------------------------------------

.                 est store Effective

.         
.         logit approve extreme##efinfo if avgeffective==1

Iteration 0:   log likelihood =  -252.0473  
Iteration 1:   log likelihood = -251.72758  
Iteration 2:   log likelihood = -251.72754  
Iteration 3:   log likelihood = -251.72754  

Logistic regression                                     Number of obs =    376
                                                        LR chi2(3)    =   0.64
                                                        Prob > chi2   = 0.8873
Log likelihood = -251.72754                             Pseudo R2     = 0.0013

--------------------------------------------------------------------------------
       approve | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
---------------+----------------------------------------------------------------
     1.extreme |   .0933596   .2944711     0.32   0.751    -.4837931    .6705124
      1.efinfo |   .0696196   .3039387     0.23   0.819    -.5260892    .6653285
               |
extreme#efinfo |
          1 1  |  -.2857315   .4231469    -0.68   0.500    -1.115084    .5436212
               |
         _cons |   .4228569   .2087035     2.03   0.043     .0138054    .8319083
--------------------------------------------------------------------------------

.                 margins, at(efinfo=(0(1)1) extreme=(0(1)1)) post

Adjusted predictions                                       Number of obs = 376
Model VCE: OIM

Expression: Pr(approve), predict()
1._at: extreme = 0
       efinfo  = 0
2._at: extreme = 0
       efinfo  = 1
3._at: extreme = 1
       efinfo  = 0
4._at: extreme = 1
       efinfo  = 1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .6041667   .0499113    12.10   0.000     .5063423     .701991
          2  |   .6206897   .0520206    11.93   0.000     .5187312    .7226481
          3  |   .6262626   .0486232    12.88   0.000     .5309628    .7215624
          4  |   .5744681   .0509959    11.26   0.000      .474518    .6744182
------------------------------------------------------------------------------

.                 est store Avg_Effective

. 
.         logit approve extreme##efinfo if ineffective==1 

Iteration 0:   log likelihood = -290.00596  
Iteration 1:   log likelihood =  -274.2933  
Iteration 2:   log likelihood = -274.27243  
Iteration 3:   log likelihood = -274.27243  

Logistic regression                                     Number of obs =    421
                                                        LR chi2(3)    =  31.47
                                                        Prob > chi2   = 0.0000
Log likelihood = -274.27243                             Pseudo R2     = 0.0543

--------------------------------------------------------------------------------
       approve | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
---------------+----------------------------------------------------------------
     1.extreme |  -.3326335   .2999912    -1.11   0.268    -.9206054    .2553384
      1.efinfo |  -1.422277   .3138105    -4.53   0.000    -2.037334   -.8072197
               |
extreme#efinfo |
          1 1  |   .5984434   .4126815     1.45   0.147    -.2103974    1.407284
               |
         _cons |   .5839479   .2317618     2.52   0.012     .1297031    1.038193
--------------------------------------------------------------------------------

.                 margins, at(efinfo=(0(1)1) extreme=(0(1)1)) post

Adjusted predictions                                       Number of obs = 421
Model VCE: OIM

Expression: Pr(approve), predict()
1._at: extreme = 0
       efinfo  = 0
2._at: extreme = 0
       efinfo  = 1
3._at: extreme = 1
       efinfo  = 0
4._at: extreme = 1
       efinfo  = 1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .6419753   .0532688    12.05   0.000     .5375703    .7463803
          2  |   .3018868   .0445894     6.77   0.000     .2144931    .3892805
          3  |      .5625    .046875    12.00   0.000     .4706267    .6543733
          4  |   .3606557   .0434744     8.30   0.000     .2754474    .4458641
------------------------------------------------------------------------------

.                 est store Ineffective

. 
.         coefplot (Effective, mcol(green) label (Highly Effective)) (Avg_Effective, mcol(black) label (Aver
> age)) (Ineffective, mcol(red)),  ylabel(`=1' "Moderate, Control" `=2' "Moderate, Treated" `=3' "Extreme, C
> ontrol" `=4' "Extreme, Treated")  title("MTurk Approval, by Ideological Extremism") legend(row(1) order(6 
> 4 2))

.                 graph save "IdeologicalExtremism_MTurk_Appendix.gph", replace
(file IdeologicalExtremism_MTurk_Appendix.gph not found)
file IdeologicalExtremism_MTurk_Appendix.gph saved

. 
.                                 
. *************
. *************   
. **  Aside  **
. *************
. *************
. 
. 
. ** Do Copartisans Update differently? (See interaction term)
.         *Generate predictor for correctly identifying their effectiveness
.                 gen correcteval=true_eff2==lesrecall if lesrecall!=.
(14 missing values generated)

.         *Models 
.                 logit correcteval copartisan##efinfo if effective==1            

Iteration 0:   log likelihood =  -111.0158  
Iteration 1:   log likelihood =  -93.23329  
Iteration 2:   log likelihood = -90.331426  
Iteration 3:   log likelihood = -90.199925  
Iteration 4:   log likelihood = -90.199253  
Iteration 5:   log likelihood = -90.199252  

Logistic regression                                     Number of obs =    231
                                                        LR chi2(3)    =  41.63
                                                        Prob > chi2   = 0.0000
Log likelihood = -90.199252                             Pseudo R2     = 0.1875

-----------------------------------------------------------------------------------
      correcteval | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
------------------+----------------------------------------------------------------
     1.copartisan |   -.693147   1.240347    -0.56   0.576    -3.124183    1.737889
         1.efinfo |   2.134166   .7881331     2.71   0.007     .5894539    3.678879
                  |
copartisan#efinfo |
             1 1  |   1.328724   1.303898     1.02   0.308    -1.226868    3.884316
                  |
            _cons |  -3.258097   .7205767    -4.52   0.000    -4.670401   -1.845792
-----------------------------------------------------------------------------------

.                 logit correcteval copartisan##efinfo if ineffective==1                  

Iteration 0:   log likelihood = -269.31953  
Iteration 1:   log likelihood = -251.72138  
Iteration 2:   log likelihood = -251.37734  
Iteration 3:   log likelihood =  -251.3763  
Iteration 4:   log likelihood =  -251.3763  

Logistic regression                                     Number of obs =    414
                                                        LR chi2(3)    =  35.89
                                                        Prob > chi2   = 0.0000
Log likelihood = -251.3763                              Pseudo R2     = 0.0666

-----------------------------------------------------------------------------------
      correcteval | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
------------------+----------------------------------------------------------------
     1.copartisan |  -.9861855   .3491675    -2.82   0.005    -1.670541   -.3018297
         1.efinfo |   .8729534   .3273734     2.67   0.008     .2313134    1.514593
                  |
copartisan#efinfo |
             1 1  |   .3116377   .4474015     0.70   0.486    -.5652531    1.188529
                  |
            _cons |  -.6337238   .2426308    -2.61   0.009    -1.109271   -.1581761
-----------------------------------------------------------------------------------

.                 
. *****************
. *****************       
. **   Appendix  **
. **   Figures   **
. *****************
. *****************
. 
.         ** Relationship between voting and actual/perceived Effectiveness
.         bysort trueeff: egen perc_vote_true=mean(voteforrep) if efinfo==0
(536 missing values generated)

.         bysort lesrecall: egen perc_vote_perceived=mean(voteforrep) if efinfo==0
(536 missing values generated)

.         graph bar (mean) perc_vote_true, ylabel(0(.2)1) over(trueeff, relabel(1"Ineffective" 2 "Average" 3
>  "Highly Effective")) ytitle(Probability of Voting for Legislator) title("MTurk (Control), Actual Effectiv
> eness") 

.                 graph save vote_actual_mturk.gph, replace 
(file vote_actual_mturk.gph not found)
file vote_actual_mturk.gph saved

.                 
.                 *gen l2=4-lesrecall /*Create this in order to get scale right on graph*/
.         graph bar (mean) perc_vote_perceived, ylabel(0(.2)1) over(l2, relabel(1"Ineffective" 2 "Average" 3
>  "Highly Effective")) ytitle(Probability of Voting for Legislator) title("MTurk (Control), Perceived Effec
> tiveness")

.                 graph save vote_perceived_mturk.gph, replace 
(file vote_perceived_mturk.gph not found)
file vote_perceived_mturk.gph saved

. 
. 
.         ** Main Treatment Effect - Vote Intention (For Figure A1 in Paper)
.         logit voteforrep efinfo if effective==1

Iteration 0:   log likelihood = -164.17249  
Iteration 1:   log likelihood = -162.43987  
Iteration 2:   log likelihood = -162.43982  
Iteration 3:   log likelihood = -162.43982  

Logistic regression                                     Number of obs =    237
                                                        LR chi2(1)    =   3.47
                                                        Prob > chi2   = 0.0627
Log likelihood = -162.43982                             Pseudo R2     = 0.0106

------------------------------------------------------------------------------
  voteforrep | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |   .4865329   .2622992     1.85   0.064     -.027564     1.00063
       _cons |  -.1988509   .1907707    -1.04   0.297    -.5727545    .1750528
------------------------------------------------------------------------------

.                 margins, at(efinfo=(0(1)1)) post

Adjusted predictions                                       Number of obs = 237
Model VCE: OIM

Expression: Pr(voteforrep), predict()
1._at: efinfo = 0
2._at: efinfo = 1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .4504505   .0472243     9.54   0.000     .3578925    .5430084
          2  |   .5714286   .0440867    12.96   0.000     .4850203    .6578369
------------------------------------------------------------------------------

.                 est store Effective

. 
.         logit voteforrep efinfo if avgeffective==1

Iteration 0:   log likelihood = -260.36264  
Iteration 1:   log likelihood = -260.33538  
Iteration 2:   log likelihood = -260.33538  

Logistic regression                                     Number of obs =    376
                                                        LR chi2(1)    =   0.05
                                                        Prob > chi2   = 0.8154
Log likelihood = -260.33538                             Pseudo R2     = 0.0001

------------------------------------------------------------------------------
  voteforrep | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |  -.0482363   .2065927    -0.23   0.815    -.4531505    .3566779
       _cons |  -.0512933   .1432701    -0.36   0.720    -.3320975    .2295109
------------------------------------------------------------------------------

.                 margins, at(efinfo=(0(1)1)) post

Adjusted predictions                                       Number of obs = 376
Model VCE: OIM

Expression: Pr(voteforrep), predict()
1._at: efinfo = 0
2._at: efinfo = 1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .4871795    .035794    13.61   0.000     .4170246    .5573344
          2  |   .4751381   .0371187    12.80   0.000     .4023867    .5478895
------------------------------------------------------------------------------

.                 est store Avg_Effective

. 
.         logit voteforrep efinfo if ineffective==1 

Iteration 0:   log likelihood = -276.46233  
Iteration 1:   log likelihood = -267.01696  
Iteration 2:   log likelihood = -266.98598  
Iteration 3:   log likelihood = -266.98598  

Logistic regression                                     Number of obs =    421
                                                        LR chi2(1)    =  18.95
                                                        Prob > chi2   = 0.0000
Log likelihood = -266.98598                             Pseudo R2     = 0.0343

------------------------------------------------------------------------------
  voteforrep | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |  -.8915215   .2071803    -4.30   0.000    -1.297587   -.4854555
       _cons |  -.0933319   .1441199    -0.65   0.517    -.3758018    .1891379
------------------------------------------------------------------------------

.                 margins, at(efinfo=(0(1)1)) post

Adjusted predictions                                       Number of obs = 421
Model VCE: OIM

Expression: Pr(voteforrep), predict()
1._at: efinfo = 0
2._at: efinfo = 1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .4766839   .0359516    13.26   0.000       .40622    .5471478
          2  |   .2719298   .0294678     9.23   0.000      .214174    .3296856
------------------------------------------------------------------------------

.                 est store Ineffective

. 
.         coefplot (Effective, mcol(green) label(Highly Effective)) (Avg_Effective, mcol(black) label(Averag
> e)) (Ineffective, mcol(red)),  ylabel(`=1' "Control" `=2' "Treated") title("MTurk, Intention to Reelect La
> wmaker", size(medium)) legend(row(1) order(6 4 2))

.                 graph save "MainTreatment_Mturk_vote.gph", replace
(file MainTreatment_Mturk_vote.gph not found)
file MainTreatment_Mturk_vote.gph saved

. 
. 
. ** Heterogeneous treatment effects by copartisanship (For Figure A2 in Paper)
.         logit voteforrep copartisan##efinfo if effective==1  

Iteration 0:   log likelihood = -161.32943  
Iteration 1:   log likelihood = -122.87405  
Iteration 2:   log likelihood = -122.83014  
Iteration 3:   log likelihood = -122.83013  

Logistic regression                                     Number of obs =    233
                                                        LR chi2(3)    =  77.00
                                                        Prob > chi2   = 0.0000
Log likelihood = -122.83013                             Pseudo R2     = 0.2386

-----------------------------------------------------------------------------------
       voteforrep | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
------------------+----------------------------------------------------------------
     1.copartisan |   2.553899   .4676011     5.46   0.000     1.637418     3.47038
         1.efinfo |   .4795728    .468286     1.02   0.306     -.438251    1.397396
                  |
copartisan#efinfo |
             1 1  |  -.1254011   .6361547    -0.20   0.844    -1.372241    1.121439
                  |
            _cons |  -1.504077   .3496029    -4.30   0.000    -2.189286   -.8188679
-----------------------------------------------------------------------------------

.                 margins, at(efinfo=(0(1)1) copartisan=(0(1)1)) post

Adjusted predictions                                       Number of obs = 233
Model VCE: OIM

Expression: Pr(voteforrep), predict()
1._at: copartisan = 0
       efinfo     = 0
2._at: copartisan = 0
       efinfo     = 1
3._at: copartisan = 1
       efinfo     = 0
4._at: copartisan = 1
       efinfo     = 1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .1818182   .0520071     3.50   0.000     .0798863    .2837502
          2  |    .264151   .0605595     4.36   0.000     .1454565    .3828455
          3  |   .7407407   .0596353    12.42   0.000     .6238577    .8576238
          4  |   .8028169   .0472187    17.00   0.000     .7102699    .8953638
------------------------------------------------------------------------------

.         est store Effective

. 
.         logit voteforrep copartisan##efinfo if avgeffective==1 

Iteration 0:   log likelihood = -256.19953  
Iteration 1:   log likelihood = -198.44244  
Iteration 2:   log likelihood = -198.23833  
Iteration 3:   log likelihood = -198.23784  
Iteration 4:   log likelihood = -198.23784  

Logistic regression                                     Number of obs =    370
                                                        LR chi2(3)    = 115.92
                                                        Prob > chi2   = 0.0000
Log likelihood = -198.23784                             Pseudo R2     = 0.2262

-----------------------------------------------------------------------------------
       voteforrep | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
------------------+----------------------------------------------------------------
     1.copartisan |    2.16626   .3320764     6.52   0.000     1.515402    2.817118
         1.efinfo |  -.5775624   .3966928    -1.46   0.145    -1.355066    .1999411
                  |
copartisan#efinfo |
             1 1  |   .6267439   .5120403     1.22   0.221    -.3768367    1.630325
                  |
            _cons |  -1.171637   .2440063    -4.80   0.000    -1.649881    -.693394
-----------------------------------------------------------------------------------

.                 margins, at(efinfo=(0(1)1) copartisan=(0(1)1)) post

Adjusted predictions                                       Number of obs = 370
Model VCE: OIM

Expression: Pr(voteforrep), predict()
1._at: copartisan = 0
       efinfo     = 0
2._at: copartisan = 0
       efinfo     = 1
3._at: copartisan = 1
       efinfo     = 0
4._at: copartisan = 1
       efinfo     = 1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .2365591   .0440673     5.37   0.000     .1501889    .3229294
          2  |   .1481481   .0394719     3.75   0.000     .0707847    .2255116
          3  |        .73   .0443959    16.44   0.000     .6429855    .8170145
          4  |   .7395833   .0447912    16.51   0.000     .6517943    .8273724
------------------------------------------------------------------------------

.         est store Avg_Effective

. 
.         logit voteforrep copartisan##efinfo if ineffective==1  

Iteration 0:   log likelihood = -274.54583  
Iteration 1:   log likelihood = -224.22219  
Iteration 2:   log likelihood =   -223.015  
Iteration 3:   log likelihood = -223.00742  
Iteration 4:   log likelihood = -223.00742  

Logistic regression                                     Number of obs =    418
                                                        LR chi2(3)    = 103.08
                                                        Prob > chi2   = 0.0000
Log likelihood = -223.00742                             Pseudo R2     = 0.1877

-----------------------------------------------------------------------------------
       voteforrep | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
------------------+----------------------------------------------------------------
     1.copartisan |   2.710903   .3953501     6.86   0.000     1.936031    3.485775
         1.efinfo |  -.2617065   .4895443    -0.53   0.593    -1.221196    .6977827
                  |
copartisan#efinfo |
             1 1  |  -1.095739   .5573819    -1.97   0.049    -2.188188   -.0032906
                  |
            _cons |  -1.871802   .3396831    -5.51   0.000    -2.537569   -1.206036
-----------------------------------------------------------------------------------

.                 margins, at(efinfo=(0(1)1) copartisan=(0(1)1)) post

Adjusted predictions                                       Number of obs = 418
Model VCE: OIM

Expression: Pr(voteforrep), predict()
1._at: copartisan = 0
       efinfo     = 0
2._at: copartisan = 0
       efinfo     = 1
3._at: copartisan = 1
       efinfo     = 0
4._at: copartisan = 1
       efinfo     = 1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .1333333   .0392523     3.40   0.001     .0564003    .2102664
          2  |   .1058824   .0333733     3.17   0.002     .0404718    .1712929
          3  |   .6982759   .0426177    16.38   0.000     .6147468    .7818049
          4  |   .3732394   .0405883     9.20   0.000     .2936879     .452791
------------------------------------------------------------------------------

.         est store Ineffective

. 
.         logit voteforrep efinfo if copartisan==1 & ineffective==1

Iteration 0:   log likelihood = -178.63813  
Iteration 1:   log likelihood = -164.86881  
Iteration 2:   log likelihood = -164.84223  
Iteration 3:   log likelihood = -164.84223  

Logistic regression                                     Number of obs =    258
                                                        LR chi2(1)    =  27.59
                                                        Prob > chi2   = 0.0000
Log likelihood = -164.84223                             Pseudo R2     = 0.0772

------------------------------------------------------------------------------
  voteforrep | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |  -1.357446   .2664976    -5.09   0.000    -1.879771   -.8351198
       _cons |   .8391011   .2022798     4.15   0.000       .44264    1.235562
------------------------------------------------------------------------------

.         ttest voteforrep if copartisan==1 & ineffective==1, by(efinfo)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. err.   Std. dev.   [95% conf. interval]
---------+--------------------------------------------------------------------
       0 |     116    .6982759    .0428025    .4609975    .6134923    .7830595
       1 |     142    .3732394    .0407319     .485377    .2927152    .4537637
---------+--------------------------------------------------------------------
Combined |     258    .5193798    .0311657    .5005954    .4580072    .5807525
---------+--------------------------------------------------------------------
    diff |            .3250364    .0593945                .2080724    .4420005
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   5.4725
H0: diff = 0                                     Degrees of freedom =      256

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 1.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 0.0000

.         
.         coefplot (Effective, mcol(green) label(Highly Effective)) (Avg_Effective, mcol(black) label(Averag
> e)) (Ineffective, mcol(red)),  ylabel(`=1' "Outparty, Control" `=2' "Outparty, Treated" `=3' "Copartisan, 
> Control" `=4' "Copartisan, Treated") title("MTurk Reelection, by Partisan Alignment", size(medium)) legend
> (row(1) order(6 4 2))

.                 graph save "Partisanship_MTurk_vote.gph", replace
(file Partisanship_MTurk_vote.gph not found)
file Partisanship_MTurk_vote.gph saved

.                 
. 
. ** Heterogenenous treatment effects by ideology (for Figure A3 in Paper)
. 
.         logit voteforrep extreme##efinfo if effective==1 

Iteration 0:   log likelihood = -164.17249  
Iteration 1:   log likelihood = -161.01533  
Iteration 2:   log likelihood = -161.01424  
Iteration 3:   log likelihood = -161.01424  

Logistic regression                                     Number of obs =    237
                                                        LR chi2(3)    =   6.32
                                                        Prob > chi2   = 0.0972
Log likelihood = -161.01424                             Pseudo R2     = 0.0192

--------------------------------------------------------------------------------
    voteforrep | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
---------------+----------------------------------------------------------------
     1.extreme |  -.4220492    .387198    -1.09   0.276    -1.180943    .3368448
      1.efinfo |   .4855078   .3927648     1.24   0.216    -.2842971    1.255313
               |
extreme#efinfo |
          1 1  |  -.0434978   .5306625    -0.08   0.935    -1.083577    .9965815
               |
         _cons |   .0425596    .291796     0.15   0.884    -.5293501    .6144693
--------------------------------------------------------------------------------

.                 margins, at(efinfo=(0(1)1) extreme=(0(1)1)) post

Adjusted predictions                                       Number of obs = 237
Model VCE: OIM

Expression: Pr(voteforrep), predict()
1._at: extreme = 0
       efinfo  = 0
2._at: extreme = 0
       efinfo  = 1
3._at: extreme = 1
       efinfo  = 0
4._at: extreme = 1
       efinfo  = 1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .5106383    .072916     7.00   0.000     .3677256     .653551
          2  |   .6290323   .0613492    10.25   0.000     .5087901    .7492744
          3  |     .40625   .0613915     6.62   0.000     .2859248    .5265752
          4  |    .515625   .0624695     8.25   0.000     .3931871    .6380629
------------------------------------------------------------------------------

.                 est store Effective

.         
.         logit voteforrep extreme##efinfo if avgeffective==1

Iteration 0:   log likelihood = -260.36264  
Iteration 1:   log likelihood = -259.73189  
Iteration 2:   log likelihood = -259.73189  

Logistic regression                                     Number of obs =    376
                                                        LR chi2(3)    =   1.26
                                                        Prob > chi2   = 0.7383
Log likelihood = -259.73189                             Pseudo R2     = 0.0024

--------------------------------------------------------------------------------
    voteforrep | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
---------------+----------------------------------------------------------------
     1.extreme |   .3101879   .2874563     1.08   0.281     -.253216    .8735918
      1.efinfo |   .1400989   .2969099     0.47   0.637    -.4418338    .7220316
               |
extreme#efinfo |
          1 1  |  -.3690284   .4139929    -0.89   0.373     -1.18044    .4423828
               |
         _cons |  -.2090918   .2052407    -1.02   0.308    -.6113561    .1931726
--------------------------------------------------------------------------------

.                 margins, at(efinfo=(0(1)1) extreme=(0(1)1)) post

Adjusted predictions                                       Number of obs = 376
Model VCE: OIM

Expression: Pr(voteforrep), predict()
1._at: extreme = 0
       efinfo  = 0
2._at: extreme = 0
       efinfo  = 1
3._at: extreme = 1
       efinfo  = 0
4._at: extreme = 1
       efinfo  = 1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .4479167   .0507534     8.83   0.000     .3484418    .5473915
          2  |   .4827586   .0535737     9.01   0.000      .377756    .5877612
          3  |   .5252525   .0501878    10.47   0.000     .4268863    .6236187
          4  |   .4680851   .0514659     9.10   0.000     .3672138    .5689564
------------------------------------------------------------------------------

.                 est store Avg_Effective

. 
.         logit voteforrep extreme##efinfo if ineffective==1 

Iteration 0:   log likelihood = -276.46233  
Iteration 1:   log likelihood = -264.70908  
Iteration 2:   log likelihood = -264.59387  
Iteration 3:   log likelihood = -264.59378  
Iteration 4:   log likelihood = -264.59378  

Logistic regression                                     Number of obs =    421
                                                        LR chi2(3)    =  23.74
                                                        Prob > chi2   = 0.0000
Log likelihood = -264.59378                             Pseudo R2     = 0.0429

--------------------------------------------------------------------------------
    voteforrep | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
---------------+----------------------------------------------------------------
     1.extreme |   .2231436     .29277     0.76   0.446    -.3506751    .7969622
      1.efinfo |  -1.116631   .3276573    -3.41   0.001    -1.758827   -.4744343
               |
extreme#efinfo |
          1 1  |    .398791   .4245806     0.94   0.348    -.4333718    1.230954
               |
         _cons |  -.2231436   .2236068    -1.00   0.318    -.6614048    .2151177
--------------------------------------------------------------------------------

.                 margins, at(efinfo=(0(1)1) extreme=(0(1)1)) post

Adjusted predictions                                       Number of obs = 421
Model VCE: OIM

Expression: Pr(voteforrep), predict()
1._at: extreme = 0
       efinfo  = 0
2._at: extreme = 0
       efinfo  = 1
3._at: extreme = 1
       efinfo  = 0
4._at: extreme = 1
       efinfo  = 1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .4444444   .0552116     8.05   0.000     .3362318    .5526571
          2  |   .2075472   .0393906     5.27   0.000     .1303431    .2847513
          3  |         .5   .0472456    10.58   0.000     .4074004    .5925996
          4  |   .3278689   .0425008     7.71   0.000     .2445688    .4111689
------------------------------------------------------------------------------

.                 est store Ineffective

. 
.         coefplot (Effective, mcol(green) label (Highly Effective)) (Avg_Effective, mcol(black) label (Aver
> age)) (Ineffective, mcol(red)),  ylabel(`=1' "Moderate, Control" `=2' "Moderate, Treated" `=3' "Extreme, C
> ontrol" `=4' "Extreme, Treated")  title("MTurk Reelection, by Ideological Extremism") legend(row(1) order(
> 6 4 2))

.                 graph save "IdeologicalExtremism_MTurk_vote.gph", replace
(file IdeologicalExtremism_MTurk_vote.gph not found)
file IdeologicalExtremism_MTurk_vote.gph saved

. 
. 
. ************************
. ************************        
. **      Appendix      **
. **       Tables       **
. ************************
. ************************
. 
. ** Appendix table A1
. reg approve efinfo if effective==1

      Source |       SS           df       MS      Number of obs   =       237
-------------+----------------------------------   F(1, 235)       =     11.40
       Model |  2.57055156         1  2.57055156   Prob > F        =    0.0009
    Residual |  53.0075075       235  .225563862   R-squared       =    0.0463
-------------+----------------------------------   Adj R-squared   =    0.0422
       Total |  55.5780591       236   .23550025   Root MSE        =    .47494

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |   .2087087   .0618247     3.38   0.001     .0869072    .3305102
       _cons |   .5135135   .0450789    11.39   0.000     .4247031    .6023239
------------------------------------------------------------------------------

. reg approve efinfo if avgeffective==1

      Source |       SS           df       MS      Number of obs   =       376
-------------+----------------------------------   F(1, 374)       =      0.14
       Model |  .032823647         1  .032823647   Prob > F        =    0.7117
    Residual |  89.7118572       374  .239871276   R-squared       =    0.0004
-------------+----------------------------------   Adj R-squared   =   -0.0023
       Total |  89.7446809       375  .239319149   Root MSE        =    .48977

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |  -.0186995   .0505506    -0.37   0.712    -.1180986    .0806995
       _cons |   .6153846   .0350729    17.55   0.000     .5464198    .6843494
------------------------------------------------------------------------------

. reg approve efinfo if ineffective==1

      Source |       SS           df       MS      Number of obs   =       421
-------------+----------------------------------   F(1, 419)       =     31.07
       Model |  7.20344274         1  7.20344274   Prob > F        =    0.0000
    Residual |  97.1433506       419  .231845705   R-squared       =    0.0690
-------------+----------------------------------   Adj R-squared   =    0.0668
       Total |  104.346793       420  .248444746   Root MSE        =     .4815

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |  -.2625216   .0470971    -5.57   0.000    -.3550977   -.1699455
       _cons |   .5958549   .0346594    17.19   0.000      .527727    .6639829
------------------------------------------------------------------------------

. 
. ** Appendix table A2
. logit approve efinfo if effective==1

Iteration 0:   log likelihood =  -156.8542  
Iteration 1:   log likelihood = -151.36124  
Iteration 2:   log likelihood = -151.34492  
Iteration 3:   log likelihood = -151.34491  

Logistic regression                                     Number of obs =    237
                                                        LR chi2(1)    =  11.02
                                                        Prob > chi2   = 0.0009
Log likelihood = -151.34491                             Pseudo R2     = 0.0351

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |   .9014442    .274996     3.28   0.001     .3624619    1.440427
       _cons |   .0540672    .189901     0.28   0.776    -.3181318    .4262663
------------------------------------------------------------------------------

. logit approve efinfo if avgeffective==1

Iteration 0:   log likelihood =  -252.0473  
Iteration 1:   log likelihood = -251.97855  
Iteration 2:   log likelihood = -251.97855  

Logistic regression                                     Number of obs =    376
                                                        LR chi2(1)    =   0.14
                                                        Prob > chi2   = 0.7108
Log likelihood = -251.97855                             Pseudo R2     = 0.0003

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |  -.0783318   .2112453    -0.37   0.711    -.4923649    .3357012
       _cons |   .4700036    .147196     3.19   0.001     .1815047    .7585025
------------------------------------------------------------------------------

. logit approve efinfo if ineffective==1

Iteration 0:   log likelihood = -290.00596  
Iteration 1:   log likelihood =   -275.346  
Iteration 2:   log likelihood = -275.33395  
Iteration 3:   log likelihood = -275.33395  

Logistic regression                                     Number of obs =    421
                                                        LR chi2(1)    =  29.34
                                                        Prob > chi2   = 0.0000
Log likelihood = -275.33395                             Pseudo R2     = 0.0506

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |   -1.08137   .2031084    -5.32   0.000    -1.479456   -.6832854
       _cons |   .3882233   .1466839     2.65   0.008     .1007282    .6757185
------------------------------------------------------------------------------

. 
. ** Appendix table A5
. reg approve efinfo if effective==1  & copartisan==1

      Source |       SS           df       MS      Number of obs   =       125
-------------+----------------------------------   F(1, 123)       =      3.69
       Model |  .468807512         1  .468807512   Prob > F        =    0.0572
    Residual |  15.6431925       123  .127180427   R-squared       =    0.0291
-------------+----------------------------------   Adj R-squared   =    0.0212
       Total |      16.112       124  .129935484   Root MSE        =    .35662

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |   .1236307   .0643931     1.92   0.057    -.0038314    .2510928
       _cons |   .7777778   .0485303    16.03   0.000      .681715    .8738406
------------------------------------------------------------------------------

. reg approve efinfo if effective==1  & copartisan==0

      Source |       SS           df       MS      Number of obs   =       108
-------------+----------------------------------   F(1, 106)       =      5.62
       Model |  1.28081126         1  1.28081126   Prob > F        =    0.0196
    Residual |  24.1543739       106  .227871452   R-squared       =    0.0504
-------------+----------------------------------   Adj R-squared   =    0.0414
       Total |  25.4351852       107  .237712011   Root MSE        =    .47736

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |   .2178388   .0918835     2.37   0.020     .0356708    .4000068
       _cons |   .2727273   .0643671     4.24   0.000     .1451133    .4003412
------------------------------------------------------------------------------

. reg approve efinfo if avgeffective==1  & copartisan==1

      Source |       SS           df       MS      Number of obs   =       196
-------------+----------------------------------   F(1, 194)       =      0.50
       Model |  .067355442         1  .067355442   Prob > F        =    0.4795
    Residual |  26.0295833       194   .13417311   R-squared       =    0.0026
-------------+----------------------------------   Adj R-squared   =   -0.0026
       Total |  26.0969388       195  .133830455   Root MSE        =     .3663

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |  -.0370833    .052339    -0.71   0.479    -.1403098    .0661431
       _cons |        .86   .0366296    23.48   0.000     .7877565    .9322435
------------------------------------------------------------------------------

. reg approve efinfo if avgeffective==1  & copartisan==0

      Source |       SS           df       MS      Number of obs   =       174
-------------+----------------------------------   F(1, 172)       =      0.38
       Model |  .086131367         1  .086131367   Prob > F        =    0.5397
    Residual |  39.2242135       172  .228047753   R-squared       =    0.0022
-------------+----------------------------------   Adj R-squared   =   -0.0036
       Total |  39.3103448       173  .227227427   Root MSE        =    .47754

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |  -.0446037   .0725778    -0.61   0.540    -.1878615     .098654
       _cons |   .3655914   .0495189     7.38   0.000     .2678483    .4633345
------------------------------------------------------------------------------

. reg approve efinfo if ineffective==1  & copartisan==1

      Source |       SS           df       MS      Number of obs   =       258
-------------+----------------------------------   F(1, 256)       =     38.49
       Model |  8.11345916         1  8.11345916   Prob > F        =    0.0000
    Residual |  53.9640602       256   .21079711   R-squared       =    0.1307
-------------+----------------------------------   Adj R-squared   =    0.1273
       Total |  62.0775194       257  .241546768   Root MSE        =    .45913

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |  -.3564837   .0574604    -6.20   0.000    -.4696391   -.2433284
       _cons |   .7931034   .0426288    18.60   0.000     .7091556    .8770513
------------------------------------------------------------------------------

. reg approve efinfo if ineffective==1  & copartisan==0

      Source |       SS           df       MS      Number of obs   =       160
-------------+----------------------------------   F(1, 158)       =      3.82
       Model |  .659215686         1  .659215686   Prob > F        =    0.0523
    Residual |  27.2407843       158  .172410027   R-squared       =    0.0236
-------------+----------------------------------   Adj R-squared   =    0.0174
       Total |        27.9       159  .175471698   Root MSE        =    .41522

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |  -.1286275   .0657811    -1.96   0.052    -.2585512    .0012963
       _cons |   .2933333   .0479458     6.12   0.000      .198636    .3880307
------------------------------------------------------------------------------

. 
. ** Appendix table A6
. logit approve copartisan##efinfo if effective==1  

Iteration 0:   log likelihood =  -153.4245  
Iteration 1:   log likelihood = -121.20867  
Iteration 2:   log likelihood = -120.42715  
Iteration 3:   log likelihood = -120.41928  
Iteration 4:   log likelihood = -120.41928  

Logistic regression                                     Number of obs =    233
                                                        LR chi2(3)    =  66.01
                                                        Prob > chi2   = 0.0000
Log likelihood = -120.41928                             Pseudo R2     = 0.2151

-----------------------------------------------------------------------------------
          approve | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
------------------+----------------------------------------------------------------
     1.copartisan |   2.233592   .4458806     5.01   0.000     1.359682    3.107502
         1.efinfo |   .9430889   .4088585     2.31   0.021     .1417411    1.744437
                  |
copartisan#efinfo |
             1 1  |   .0171207   .6578679     0.03   0.979    -1.272277    1.306518
                  |
            _cons |  -.9808293    .302765    -3.24   0.001    -1.574238   -.3874207
-----------------------------------------------------------------------------------

. logit approve copartisan##efinfo if avgeffective==1  

Iteration 0:   log likelihood = -247.74713  
Iteration 1:   log likelihood = -197.98222  
Iteration 2:   log likelihood = -197.22138  
Iteration 3:   log likelihood =  -197.2201  
Iteration 4:   log likelihood =  -197.2201  

Logistic regression                                     Number of obs =    370
                                                        LR chi2(3)    = 101.05
                                                        Prob > chi2   = 0.0000
Log likelihood = -197.2201                              Pseudo R2     = 0.2039

-----------------------------------------------------------------------------------
          approve | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
------------------+----------------------------------------------------------------
     1.copartisan |   2.366467   .3597463     6.58   0.000     1.661377    3.071557
         1.efinfo |  -.1980597   .3209428    -0.62   0.537     -.827096    .4309766
                  |
copartisan#efinfo |
             1 1  |  -.0809957   .5074865    -0.16   0.873    -1.075651    .9136595
                  |
            _cons |  -.5511769   .2153159    -2.56   0.010    -.9731882   -.1291656
-----------------------------------------------------------------------------------

. logit approve copartisan##efinfo if ineffective==1  

Iteration 0:   log likelihood = -288.00586  
Iteration 1:   log likelihood = -239.94035  
Iteration 2:   log likelihood = -239.83323  
Iteration 3:   log likelihood = -239.83309  
Iteration 4:   log likelihood = -239.83309  

Logistic regression                                     Number of obs =    418
                                                        LR chi2(3)    =  96.35
                                                        Prob > chi2   = 0.0000
Log likelihood = -239.83309                             Pseudo R2     = 0.1673

-----------------------------------------------------------------------------------
          approve | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
------------------+----------------------------------------------------------------
     1.copartisan |   2.222984    .341846     6.50   0.000     1.552978     2.89299
         1.efinfo |  -.7443731    .387086    -1.92   0.054    -1.503048    .0143015
                  |
copartisan#efinfo |
             1 1  |  -.8542539   .4806254    -1.78   0.076    -1.796262    .0877546
                  |
            _cons |  -.8792495   .2536187    -3.47   0.001    -1.376333   -.3821658
-----------------------------------------------------------------------------------

.         
. ** Appendix table A9
. reg approve efinfo if extreme==1 & effective==1

      Source |       SS           df       MS      Number of obs   =       128
-------------+----------------------------------   F(1, 126)       =      3.86
       Model |    .9453125         1    .9453125   Prob > F        =    0.0517
    Residual |   30.859375       126  .244915675   R-squared       =    0.0297
-------------+----------------------------------   Adj R-squared   =    0.0220
       Total |  31.8046875       127   .25043061   Root MSE        =    .49489

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |    .171875   .0874849     1.96   0.052    -.0012551    .3450051
       _cons |    .453125   .0618612     7.32   0.000     .3307035    .5755465
------------------------------------------------------------------------------

. reg approve efinfo if extreme==0 & effective==1

      Source |       SS           df       MS      Number of obs   =       109
-------------+----------------------------------   F(1, 107)       =      7.23
       Model |  1.37558323         1  1.37558323   Prob > F        =    0.0083
    Residual |   20.367536       107  .190350804   R-squared       =    0.0633
-------------+----------------------------------   Adj R-squared   =    0.0545
       Total |  21.7431193       108  .201325178   Root MSE        =    .43629

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |    .226836   .0843812     2.69   0.008       .05956    .3941119
       _cons |   .5957447   .0636397     9.36   0.000     .4695863    .7219031
------------------------------------------------------------------------------

. reg approve efinfo if extreme==1 & avgeffective==1

      Source |       SS           df       MS      Number of obs   =       193
-------------+----------------------------------   F(1, 191)       =      0.54
       Model |   .12935217         1   .12935217   Prob > F        =    0.4653
    Residual |  46.1504406       191  .241625343   R-squared       =    0.0028
-------------+----------------------------------   Adj R-squared   =   -0.0024
       Total |  46.2797927       192  .241040587   Root MSE        =    .49155

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |  -.0517945   .0707894    -0.73   0.465     -.191424    .0878349
       _cons |   .6262626    .049403    12.68   0.000      .528817    .7237082
------------------------------------------------------------------------------

. reg approve efinfo if extreme==0 & avgeffective==1

      Source |       SS           df       MS      Number of obs   =       183
-------------+----------------------------------   F(1, 181)       =      0.05
       Model |  .012459959         1  .012459959   Prob > F        =    0.8200
    Residual |   43.441092       181  .240006033   R-squared       =    0.0003
-------------+----------------------------------   Adj R-squared   =   -0.0052
       Total |  43.4535519       182   .23875578   Root MSE        =     .4899

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |    .016523   .0725173     0.23   0.820     -.126565    .1596109
       _cons |   .6041667   .0500006    12.08   0.000     .5055076    .7028258
------------------------------------------------------------------------------

. reg approve efinfo if extreme==1 & ineffective==1

      Source |       SS           df       MS      Number of obs   =       234
-------------+----------------------------------   F(1, 232)       =      9.91
       Model |  2.37900203         1  2.37900203   Prob > F        =    0.0019
    Residual |  55.6936475       232  .240058826   R-squared       =    0.0410
-------------+----------------------------------   Adj R-squared   =    0.0368
       Total |  58.0726496       233  .249238839   Root MSE        =    .48996

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |  -.2018443   .0641177    -3.15   0.002    -.3281716   -.0755169
       _cons |      .5625   .0462967    12.15   0.000     .4712843    .6537157
------------------------------------------------------------------------------

. reg approve efinfo if extreme==0 & ineffective==1

      Source |       SS           df       MS      Number of obs   =       187
-------------+----------------------------------   F(1, 185)       =     23.99
       Model |  5.31047309         1  5.31047309   Prob > F        =    0.0000
    Residual |  40.9569066       185  .221388684   R-squared       =    0.1148
-------------+----------------------------------   Adj R-squared   =    0.1100
       Total |  46.2673797       186  .248749353   Root MSE        =    .47052

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |  -.3400885   .0694389    -4.90   0.000    -.4770825   -.2030945
       _cons |   .6419753     .05228    12.28   0.000     .5388338    .7451169
------------------------------------------------------------------------------

. 
. ** Appendix table A10
. logit approve extreme##efinfo if effective==1  

Iteration 0:   log likelihood =  -156.8542  
Iteration 1:   log likelihood = -147.26461  
Iteration 2:   log likelihood = -147.11307  
Iteration 3:   log likelihood = -147.11305  
Iteration 4:   log likelihood = -147.11305  

Logistic regression                                     Number of obs =    237
                                                        LR chi2(3)    =  19.48
                                                        Prob > chi2   = 0.0002
Log likelihood = -147.11305                             Pseudo R2     = 0.0621

--------------------------------------------------------------------------------
       approve | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
---------------+----------------------------------------------------------------
     1.extreme |  -.5758178   .3891016    -1.48   0.139    -1.338443    .1868074
      1.efinfo |   1.146165   .4459404     2.57   0.010     .2721378    2.020192
               |
extreme#efinfo |
          1 1  |   -.447287   .5732222    -0.78   0.435    -1.570782    .6762078
               |
         _cons |   .3877655   .2972303     1.30   0.192    -.1947952    .9703263
--------------------------------------------------------------------------------

. logit approve extreme##efinfo if avgeffective==1  

Iteration 0:   log likelihood =  -252.0473  
Iteration 1:   log likelihood = -251.72758  
Iteration 2:   log likelihood = -251.72754  
Iteration 3:   log likelihood = -251.72754  

Logistic regression                                     Number of obs =    376
                                                        LR chi2(3)    =   0.64
                                                        Prob > chi2   = 0.8873
Log likelihood = -251.72754                             Pseudo R2     = 0.0013

--------------------------------------------------------------------------------
       approve | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
---------------+----------------------------------------------------------------
     1.extreme |   .0933596   .2944711     0.32   0.751    -.4837931    .6705124
      1.efinfo |   .0696196   .3039387     0.23   0.819    -.5260892    .6653285
               |
extreme#efinfo |
          1 1  |  -.2857315   .4231469    -0.68   0.500    -1.115084    .5436212
               |
         _cons |   .4228569   .2087035     2.03   0.043     .0138054    .8319083
--------------------------------------------------------------------------------

. logit approve extreme##efinfo if ineffective==1  

Iteration 0:   log likelihood = -290.00596  
Iteration 1:   log likelihood =  -274.2933  
Iteration 2:   log likelihood = -274.27243  
Iteration 3:   log likelihood = -274.27243  

Logistic regression                                     Number of obs =    421
                                                        LR chi2(3)    =  31.47
                                                        Prob > chi2   = 0.0000
Log likelihood = -274.27243                             Pseudo R2     = 0.0543

--------------------------------------------------------------------------------
       approve | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
---------------+----------------------------------------------------------------
     1.extreme |  -.3326335   .2999912    -1.11   0.268    -.9206054    .2553384
      1.efinfo |  -1.422277   .3138105    -4.53   0.000    -2.037334   -.8072197
               |
extreme#efinfo |
          1 1  |   .5984434   .4126815     1.45   0.147    -.2103974    1.407284
               |
         _cons |   .5839479   .2317618     2.52   0.012     .1297031    1.038193
--------------------------------------------------------------------------------

. 
. ** Appendix table A13
. reg approve4 efinfo if effective==1

      Source |       SS           df       MS      Number of obs   =       237
-------------+----------------------------------   F(1, 235)       =      8.08
       Model |  .441422652         1  .441422652   Prob > F        =    0.0049
    Residual |  12.8331414       235  .054609112   R-squared       =    0.0333
-------------+----------------------------------   Adj R-squared   =    0.0291
       Total |  13.2745641       236  .056248153   Root MSE        =    .23369

------------------------------------------------------------------------------
    approve4 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |   .0864878   .0304201     2.84   0.005     .0265569    .1464186
       _cons |    .495036   .0221805    22.32   0.000      .451338     .538734
------------------------------------------------------------------------------

. reg approve4 efinfo if avgeffective==1

      Source |       SS           df       MS      Number of obs   =       376
-------------+----------------------------------   F(1, 374)       =      0.00
       Model |  .000031815         1  .000031815   Prob > F        =    0.9812
    Residual |  21.4304344       374  .057300627   R-squared       =    0.0000
-------------+----------------------------------   Adj R-squared   =   -0.0027
       Total |  21.4304662       375   .05714791   Root MSE        =    .23938

------------------------------------------------------------------------------
    approve4 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |  -.0005822   .0247068    -0.02   0.981    -.0491639    .0479995
       _cons |    .515759    .017142    30.09   0.000     .4820521    .5494658
------------------------------------------------------------------------------

. reg approve4 efinfo if ineffective==1

      Source |       SS           df       MS      Number of obs   =       421
-------------+----------------------------------   F(1, 419)       =     25.39
       Model |  1.40961893         1  1.40961893   Prob > F        =    0.0000
    Residual |  23.2640047       419  .055522684   R-squared       =    0.0571
-------------+----------------------------------   Adj R-squared   =    0.0549
       Total |  24.6736236       420  .058746723   Root MSE        =    .23563

------------------------------------------------------------------------------
    approve4 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |  -.1161304   .0230478    -5.04   0.000    -.1614342   -.0708266
       _cons |   .5090207   .0169612    30.01   0.000     .4756811    .5423604
------------------------------------------------------------------------------

. 
. ** Appendix table A15
. reg approve4 efinfo if effective==1  & copartisan==1

      Source |       SS           df       MS      Number of obs   =       125
-------------+----------------------------------   F(1, 123)       =      4.28
       Model |  .155113245         1  .155113245   Prob > F        =    0.0406
    Residual |  4.45538275       123  .036222624   R-squared       =    0.0336
-------------+----------------------------------   Adj R-squared   =    0.0258
       Total |  4.61049599       124  .037181419   Root MSE        =    .19032

------------------------------------------------------------------------------
    approve4 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |   .0711137   .0343652     2.07   0.041     .0030899    .1391375
       _cons |   .6044074   .0258996    23.34   0.000     .5531407    .6556741
------------------------------------------------------------------------------

. reg approve4 efinfo if effective==1  & copartisan==0

      Source |       SS           df       MS      Number of obs   =       108
-------------+----------------------------------   F(1, 106)       =      1.52
       Model |  .075941691         1  .075941691   Prob > F        =    0.2208
    Residual |  5.30649972       106  .050061318   R-squared       =    0.0141
-------------+----------------------------------   Adj R-squared   =    0.0048
       Total |  5.38244141       107  .050303191   Root MSE        =    .22374

------------------------------------------------------------------------------
    approve4 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |   .0530436   .0430669     1.23   0.221    -.0323408    .1384279
       _cons |   .4056546   .0301696    13.45   0.000     .3458404    .4654688
------------------------------------------------------------------------------

. reg approve4 efinfo if avgeffective==1  & copartisan==1

      Source |       SS           df       MS      Number of obs   =       196
-------------+----------------------------------   F(1, 194)       =      0.04
       Model |  .001274376         1  .001274376   Prob > F        =    0.8390
    Residual |  5.97406937       194  .030794172   R-squared       =    0.0002
-------------+----------------------------------   Adj R-squared   =   -0.0049
       Total |  5.97534374       195  .030642788   Root MSE        =    .17548

------------------------------------------------------------------------------
    approve4 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |  -.0051008   .0250742    -0.20   0.839    -.0545538    .0443522
       _cons |     .62608   .0175483    35.68   0.000     .5914701    .6606899
------------------------------------------------------------------------------

. reg approve4 efinfo if avgeffective==1  & copartisan==0

      Source |       SS           df       MS      Number of obs   =       174
-------------+----------------------------------   F(1, 172)       =      0.08
       Model |  .004781214         1  .004781214   Prob > F        =    0.7777
    Residual |  10.2828378       172  .059783941   R-squared       =    0.0005
-------------+----------------------------------   Adj R-squared   =   -0.0053
       Total |   10.287619       173  .059466006   Root MSE        =    .24451

------------------------------------------------------------------------------
    approve4 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |   -.010509   .0371606    -0.28   0.778    -.0838585    .0628406
       _cons |   .4010645   .0253543    15.82   0.000      .351019    .4511101
------------------------------------------------------------------------------

. reg approve4 efinfo if ineffective==1  & copartisan==1

      Source |       SS           df       MS      Number of obs   =       258
-------------+----------------------------------   F(1, 256)       =     29.65
       Model |  1.25812728         1  1.25812728   Prob > F        =    0.0000
    Residual |  10.8634795       256  .042435467   R-squared       =    0.1038
-------------+----------------------------------   Adj R-squared   =    0.1003
       Total |  12.1216068       257  .047165785   Root MSE        =      .206

------------------------------------------------------------------------------
    approve4 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |   -.140378   .0257811    -5.44   0.000     -.191148    -.089608
       _cons |   .6000259   .0191265    31.37   0.000     .5623606    .6376912
------------------------------------------------------------------------------

. reg approve4 efinfo if ineffective==1  & copartisan==0

      Source |       SS           df       MS      Number of obs   =       160
-------------+----------------------------------   F(1, 158)       =      5.95
       Model |  .301381633         1  .301381633   Prob > F        =    0.0159
    Residual |  8.00905607       158  .050690228   R-squared       =    0.0363
-------------+----------------------------------   Adj R-squared   =    0.0302
       Total |  8.31043771       159  .052266904   Root MSE        =    .22514

------------------------------------------------------------------------------
    approve4 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |  -.0869718   .0356683    -2.44   0.016    -.1574199   -.0165237
       _cons |     .37296   .0259975    14.35   0.000     .3216126    .4243074
------------------------------------------------------------------------------

.  
.  ** Appendix table A17
. reg approve4 efinfo if extreme==1 & effective==1

      Source |       SS           df       MS      Number of obs   =       128
-------------+----------------------------------   F(1, 126)       =      1.96
       Model |        .125         1        .125   Prob > F        =    0.1640
    Residual |  8.03684094       126  .063784452   R-squared       =    0.0153
-------------+----------------------------------   Adj R-squared   =    0.0075
       Total |  8.16184094       127  .064266464   Root MSE        =    .25256

------------------------------------------------------------------------------
    approve4 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |      .0625    .044646     1.40   0.164    -.0258531    .1508531
       _cons |   .4891563   .0315695    15.49   0.000     .4266812    .5516313
------------------------------------------------------------------------------

. reg approve4 efinfo if extreme==0 & effective==1

      Source |       SS           df       MS      Number of obs   =       109
-------------+----------------------------------   F(1, 107)       =      7.31
       Model |  .319448611         1  .319448611   Prob > F        =    0.0080
    Residual |  4.67504819       107  .043692039   R-squared       =    0.0640
-------------+----------------------------------   Adj R-squared   =    0.0552
       Total |   4.9944968       108  .046245341   Root MSE        =    .20903

------------------------------------------------------------------------------
    approve4 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |   .1093123   .0404268     2.70   0.008     .0291708    .1894538
       _cons |   .5030426   .0304896    16.50   0.000     .4426004    .5634847
------------------------------------------------------------------------------

. reg approve4 efinfo if extreme==1 & avgeffective==1

      Source |       SS           df       MS      Number of obs   =       193
-------------+----------------------------------   F(1, 191)       =      0.00
       Model |  .000284957         1  .000284957   Prob > F        =    0.9463
    Residual |  11.9798975       191  .062721976   R-squared       =    0.0000
-------------+----------------------------------   Adj R-squared   =   -0.0052
       Total |  11.9801824       192  .062396783   Root MSE        =    .25044

------------------------------------------------------------------------------
    approve4 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |    .002431   .0360667     0.07   0.946    -.0687093    .0735713
       _cons |    .511303   .0251705    20.31   0.000     .4616551    .5609509
------------------------------------------------------------------------------

. reg approve4 efinfo if extreme==0 & avgeffective==1

      Source |       SS           df       MS      Number of obs   =       183
-------------+----------------------------------   F(1, 181)       =      0.01
       Model |  .000597592         1  .000597592   Prob > F        =    0.9149
    Residual |  9.44613706       181  .052188603   R-squared       =    0.0001
-------------+----------------------------------   Adj R-squared   =   -0.0055
       Total |  9.44673465       182  .051905135   Root MSE        =    .22845

------------------------------------------------------------------------------
    approve4 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |  -.0036185   .0338157    -0.11   0.915    -.0703422    .0631051
       _cons |   .5203542   .0233159    22.32   0.000     .4743482    .5663601
------------------------------------------------------------------------------

. reg approve4 efinfo if extreme==1 & ineffective==1

      Source |       SS           df       MS      Number of obs   =       234
-------------+----------------------------------   F(1, 232)       =      8.28
       Model |  .527815454         1  .527815454   Prob > F        =    0.0044
    Residual |  14.7826436       232  .063718292   R-squared       =    0.0345
-------------+----------------------------------   Adj R-squared   =    0.0303
       Total |  15.3104591       233  .065710125   Root MSE        =    .25242

------------------------------------------------------------------------------
    approve4 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |  -.0950736   .0330332    -2.88   0.004    -.1601571   -.0299902
       _cons |   .4935982   .0238519    20.69   0.000     .4466042    .5405922
------------------------------------------------------------------------------

. reg approve4 efinfo if extreme==0 & ineffective==1

      Source |       SS           df       MS      Number of obs   =       187
-------------+----------------------------------   F(1, 185)       =     20.93
       Model |  .951288829         1  .951288829   Prob > F        =    0.0000
    Residual |  8.40955608       185   .04545706   R-squared       =    0.1016
-------------+----------------------------------   Adj R-squared   =    0.0968
       Total |  9.36084491       186  .050327123   Root MSE        =    .21321

------------------------------------------------------------------------------
    approve4 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |    -.14394   .0314649    -4.57   0.000    -.2060161   -.0818639
       _cons |   .5303457   .0236896    22.39   0.000     .4836091    .5770822
------------------------------------------------------------------------------

.  
. 
end of do-file

. do "/Users/daniel.butler/Dropbox/BHVW_Replication/Code/CCESReplication.do"

. clear all

. set more off

. set scheme s1mono

. cd "~/Dropbox/BHVW_Replication/"
/Users/daniel.butler/Dropbox/BHVW_Replication

. 
. use "CCES/CCES16_UVA_OUTPUT_Feb2017_13.dta"
( )

. *ssc install coefplot
. *bring in LES data by rep name
. merge m:1 CurrentHouseName using "CCES/matchfile_UPDATE_full.dta"

    Result                      Number of obs
    -----------------------------------------
    Not matched                            12
        from master                        12  (_merge==1)
        from using                          0  (_merge==2)

    Matched                             1,488  (_merge==3)
    -----------------------------------------

. cd "~/Dropbox/BHVW_Replication/Code/Results/"
/Users/daniel.butler/Dropbox/BHVW_Replication/Code/Results

. 
. 
. *drop sample if the MC did not serve in 113th (this is not a control group in the experiment)
. drop if control==1
(225 observations deleted)

. *drop sample if no effectiveness information exists
. drop if effectiveness==""
(12 observations deleted)

. * fix errors in CCES data
. replace HouseCand1Name="Alcee Hastings" if CurrentHouseName=="Alcee Hastings"
(5 real changes made)

. replace HouseCand1Name="Ben Lujan" if CurrentHouseName=="Ben Lujan"
(3 real changes made)

. replace HouseCand1Name="Bill Keating" if CurrentHouseName=="Bill Keating"
(1 real change made)

. replace HouseCand1Name="Bob Brady" if CurrentHouseName=="Bob Brady"
(3 real changes made)

. replace HouseCand2Name="Chris Smith" if CurrentHouseName=="Chris Smith"
(1 real change made)

. replace HouseCand2Name="Dennis Ross" if CurrentHouseName=="Dennis Ross"
(2 real changes made)

. replace HouseCand2Name="Duncan D. Hunter" if CurrentHouseName=="Duncan D. Hunter"
(3 real changes made)

. replace HouseCand2Name="Ed Royce" if CurrentHouseName=="Ed Royce"
(2 real changes made)

. replace HouseCand1Name="Eddie Johnson" if CurrentHouseName=="Eddie Johnson"
(2 real changes made)

. replace HouseCand1Name="Emanuel Cleaver" if CurrentHouseName=="Emanuel Cleaver"
(8 real changes made)

. replace HouseCand1Name="Filemon Vela Jr." if CurrentHouseName=="Filemon Vela Jr."
(3 real changes made)

. replace HouseCand1Name="Frederica Wilson" if CurrentHouseName=="Frederica Wilson" & HouseCand1Name=="Frede
> rica S. Wilson"
(2 real changes made)

. replace HouseCand1Name="Gerry Connolly" if CurrentHouseName=="Gerry Connolly" & HouseCand1Name=="Gerald Co
> nnolly"
(2 real changes made)

. replace HouseCand1Name="Jan Schakowsky" if CurrentHouseName=="Jan Schakowsky"
(5 real changes made)

. replace HouseCand2Name="Jason T. Smith" if CurrentHouseName=="Jason T. Smith"
(4 real changes made)

. replace HouseCand2Name="Jimmy Duncan" if CurrentHouseName=="Jimmy Duncan"
(2 real changes made)

. replace HouseCand1Name="Joe Kennedy" if CurrentHouseName=="Joe Kennedy"
(2 real changes made)

. replace HouseCand1Name="John B. Larson" if CurrentHouseName=="John B. Larson"
(4 real changes made)

. replace HouseCand1Name="John Conyers" if CurrentHouseName=="John Conyers"
(4 real changes made)

. replace HouseCand2Name="Lamar S. Smith" if CurrentHouseName=="Lamar S. Smith"
(1 real change made)

. replace HouseCand1Name="Michael Doyle" if CurrentHouseName=="Michael Doyle"
(5 real changes made)

. replace HouseCand2Name="Pat Meehan" if CurrentHouseName=="Pat Meehan"
(2 real changes made)

. replace HouseCand2Name="Pat Tiberi" if CurrentHouseName=="Pat Tiberi"
(5 real changes made)

. replace HouseCand1Name="Rick Larsen" if CurrentHouseName=="Rick Larsen"
(5 real changes made)

. replace HouseCand2Name="Rob Wittman" if CurrentHouseName=="Rob Wittman"
(5 real changes made)

. replace HouseCand2Name="Timothy F. Murphy" if CurrentHouseName=="Timothy F. Murphy"
(4 real changes made)

. replace HouseCand2Name="Walter Jones" if CurrentHouseName=="Walter Jones"
(4 real changes made)

. replace HouseCand1Name="William Clay" if CurrentHouseName=="William Clay"
(3 real changes made)

. 
. *create an effectiveness treatment dummy
. gen efinfo=0

. replace efinfo=1 if UVA307rand==3| UVA307rand==4
(651 real changes made)

. label var efinfo "1 = received informational treatment"

. 
. *creating separate variables for actual effectiveness
. gen effective= effectiveness=="highly effective" if !missing("effectiveness")

. label var effective "1 = Actually highly effective"

. gen avgeffective=effectiveness=="average in effectiveness" if !missing("effectiveness")

. label var avgeffective "1 = Actually average effectiveness"

. gen ineffective=effectiveness=="not effective" if !missing("effectiveness")

. label var ineffective "1 = Actually ineffective"

. 
. ** To calculate correlation
.         gen effective_numeric=effective*1 + avgeffective*2 + ineffective*3

. ** For the tabplot graph reverse the scale
.         gen effective_graph=4-effective_numeric

.         label var effective_graph "1 = Actually ineffective, 2 = Average, 3 = Highly"

. 
. *creating separate variables for perceived effectiveness
. gen r_effective=UVA313==1 if UVA313!=.
(8 missing values generated)

. label var r_effective "1 = Perceived highly effective"

. gen r_avgeffective=UVA313==2 if UVA313!=.
(8 missing values generated)

. label var r_avgeffective "1 = Perceived average effectiveness"

. gen r_ineffective=UVA313==3 if UVA313!=.
(8 missing values generated)

. label var r_ineffective "1 = Perceived ineffective"

. 
. *create party dummies (inc. leaners)
. gen dem=0

. replace dem=1 if pid3==1 | pid7==3
(626 real changes made)

. label var dem "1 = Democrat, including leaners"

. gen rep=0

. replace rep=1 if pid3==2 | pid7==5
(390 real changes made)

. label var rep "1 = Republican, including leaners"

. 
. *dichotomize approval
. gen approve=.
(1,263 missing values generated)

. replace approve=1 if UVA307==1 | UVA307==2
(729 real changes made)

. replace approve=0 if UVA307==3 | UVA307==4
(525 real changes made)

. label var approve "Dichotomous job approval"

. 
. *4 category approval
. gen approve4=.
(1,263 missing values generated)

. replace approve4=0 if UVA307==4
(160 real changes made)

. replace approve4=.333 if UVA307==3
(365 real changes made)

. replace approve4=.666 if UVA307==2
(590 real changes made)

. replace approve4=1 if UVA307==1
(139 real changes made)

. label var approve4 "Four category job approval"

. 
. *w2 approval
. gen approvew2=.
(1,263 missing values generated)

. replace approvew2=1 if UVA404==1 | UVA404==2
(736 real changes made)

. replace approvew2=0 if UVA404==3 | UVA404==4
(334 real changes made)

. label var approvew2 "Wave 2, dichotomous approval"

. 
. *vote intent
. gen voteforrep=0

. replace voteforrep=1 if UVA312==1 & HouseCand1Name==CurrentHouseName
(262 real changes made)

. replace voteforrep=1 if UVA312==2 & HouseCand2Name==CurrentHouseName
(222 real changes made)

. label var voteforrep "1 = plan to vote for incumbent"

. drop if HouseCand2Name!=CurrentHouseName & HouseCand1Name!=CurrentHouseName
(207 observations deleted)

. 
. **Reported vote, Wave 2
. gen voteforrepw2=0

. replace voteforrepw2=1 if CC16_412==1 & HouseCand1Name==CurrentHouseName
(230 real changes made)

. replace voteforrepw2=1 if CC16_412==2 & HouseCand2Name==CurrentHouseName
(226 real changes made)

. label var voteforrepw2 "1 = reported voting for incumbent"

. 
. *create the three treatment vars
. gen treatef=0

. replace treatef=1 if efinfo==1 & effectiveness=="highly effective"
(127 real changes made)

. label var treatef "1 = Received treatment and highly effective"

. 
. gen treatavgef=0

. replace treatavgef=1 if efinfo==1 & effectiveness=="average in effectiveness"
(186 real changes made)

. label var treatavgef "1 = Received treatment and average effectiveness"

. 
. gen treatinef=0

. replace treatinef=1 if efinfo==1 & effectiveness=="not effective"
(223 real changes made)

. label var treatinef "1 = Received treatment and ineffective"

. 
. *identify copartisans
. gen copartisan=.
(1,056 missing values generated)

. replace copartisan=1 if dem==1 & party=="Democrat"
(299 real changes made)

. replace copartisan=1 if rep==1 & party=="Republican"
(219 real changes made)

. replace copartisan=0 if dem==1 & party=="Republican"
(225 real changes made)

. replace copartisan=0 if rep==1 & party=="Democrat"
(103 real changes made)

. label var copartisan "1 = Representative is from respondent's party"

. 
. *********************
. *********************
. ******ANALYSIS*******
. *********************
. *********************
. 
. ** Outcome = Approve of MC
. 
. ** Basic Levels of Knowledge in Control Group (For Figure 1 in paper)
.         tab UVA313 effective_numeric if efinfo==0, chi

    W1 Post-Treatment |        effective_numeric
       Effective Eval |         1          2          3 |     Total
----------------------+---------------------------------+----------
     Highly effective |        20         28         34 |        82 
Average in effectiven |        75        109        125 |       309 
        Not effective |        27         43         53 |       123 
----------------------+---------------------------------+----------
                Total |       122        180        212 |       514 

          Pearson chi2(4) =   0.3904   Pr = 0.983

.         corr  UVA313 effective_numeric if efinfo==0
(obs=514)

             |   UVA313 effect~c
-------------+------------------
      UVA313 |   1.0000
effective_~c |   0.0190   1.0000


.         tabplot UVA313 effective_graph if efinfo==0, percent(effective_graph)  horizontal ytitle("Perceive
> d" "Effectiveness") ylab(1 "Ineffective" 2 "Average" 3 "Highly Effective") xtitle( " " "Actual Effectivene
> ss") xlabel(1 "Ineffective" 2 "Average" 3 "Highly Effective") showval(format(%2.1f) mlabsize(vsmall) offse
> t(0.25)) title("CCES Survey (Control Group)", size(medium)) subtitle(" ") 

.                 graph save knowledge_cces, replace
(file knowledge_cces.gph not found)
file knowledge_cces.gph saved

. 
. ** Relationship between approval and actual/perceived Effectiveness in Control Group (For Figure 2 in pape
> r)
.         bysort effective_numeric: egen perc_approve_true=mean(approve) if efinfo==0
(536 missing values generated)

.         bysort UVA313: egen perc_approve_perceived=mean(approve) if efinfo==0
(536 missing values generated)

.         graph bar (mean) perc_approve_true, ylabel(0(.2)1) over(effective_graph, relabel(1"Ineffective" 2 
> "Average" 3 "Highly Effective")) ytitle(Percent Approving of Legislator) title("CCES, Actual Effectiveness
> ") 

.                 graph save approve_actual_cces.gph, replace 
(file approve_actual_cces.gph not found)
file approve_actual_cces.gph saved

.                 
.                 gen l2=4-UVA313 /*Create this in order to get scale right on graph*/
(7 missing values generated)

.         graph bar (mean) perc_approve_perceived, ylabel(0(.2)1) over(l2, relabel(1"Ineffective" 2 "Average
> " 3 "Highly Effective")) ytitle(Percent Approving of Legislator) title("CCES, Perceived Effectiveness")

.                 graph save approve_perceived_cces.gph, replace 
(file approve_perceived_cces.gph not found)
file approve_perceived_cces.gph saved

. 
. ** Approval not based on raw LES either in Control Group
.         reg approve les if efinfo==0

      Source |       SS           df       MS      Number of obs   =       515
-------------+----------------------------------   F(1, 513)       =      0.20
       Model |  .045672373         1  .045672373   Prob > F        =    0.6575
    Residual |  119.053357       513   .23207282   R-squared       =    0.0004
-------------+----------------------------------   Adj R-squared   =   -0.0016
       Total |  119.099029       514  .231710173   Root MSE        =    .48174

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         les |   -.008058   .0181641    -0.44   0.658    -.0437431    .0276271
       _cons |   .6448286   .0277596    23.23   0.000     .5902921    .6993651
------------------------------------------------------------------------------

.         * Or on majority status or chair positions
.         reg approve les chair subchr majority seniority if efinfo==0

      Source |       SS           df       MS      Number of obs   =       515
-------------+----------------------------------   F(5, 509)       =      3.56
       Model |  4.02103554         5  .804207109   Prob > F        =    0.0036
    Residual |  115.077994       509  .226086431   R-squared       =    0.0338
-------------+----------------------------------   Adj R-squared   =    0.0243
       Total |  119.099029       514  .231710173   Root MSE        =    .47549

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         les |  -.0012253   .0210496    -0.06   0.954    -.0425802    .0401296
       chair |  -.0149404   .1329056    -0.11   0.911    -.2760515    .2461708
      subchr |  -.1035329   .0598655    -1.73   0.084    -.2211467     .014081
    majority |   -.049279   .0535284    -0.92   0.358    -.1544428    .0558848
   seniority |   .0133912   .0052379     2.56   0.011     .0031007    .0236817
       _cons |   .6183528   .0479942    12.88   0.000     .5240616     .712644
------------------------------------------------------------------------------

.         * Although somewhat on seniority
.         reg approve seniority if efinfo==0

      Source |       SS           df       MS      Number of obs   =       515
-------------+----------------------------------   F(1, 513)       =     10.08
       Model |  2.29605707         1  2.29605707   Prob > F        =    0.0016
    Residual |  116.802972       513  .227686105   R-squared       =    0.0193
-------------+----------------------------------   Adj R-squared   =    0.0174
       Total |  119.099029       514  .231710173   Root MSE        =    .47716

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
   seniority |   .0150495   .0047391     3.18   0.002      .005739      .02436
       _cons |   .5578176   .0325911    17.12   0.000     .4937892    .6218459
------------------------------------------------------------------------------

.                 
. ** Perceived effectiveness also not based on raw LES either in Control Group
.         gen r_eff3=.
(1,056 missing values generated)

.         replace r_eff3=1 if r_ineffective==1
(270 real changes made)

.         replace r_eff3=2 if r_avgeffective==1
(622 real changes made)

.         replace r_eff3=3 if r_effective==1
(157 real changes made)

.         label var r_eff3 "1 = Perceive ineffective, 2 = perceived average, 3 = perceived highly effective"

.         reg r_eff3 les if efinfo==0

      Source |       SS           df       MS      Number of obs   =       514
-------------+----------------------------------   F(1, 512)       =      0.00
       Model |  .000941727         1  .000941727   Prob > F        =    0.9610
    Residual |   201.72863       512  .394001231   R-squared       =    0.0000
-------------+----------------------------------   Adj R-squared   =   -0.0019
       Total |  201.729572       513  .393235033   Root MSE        =     .6277

------------------------------------------------------------------------------
      r_eff3 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         les |   .0011627   .0237827     0.05   0.961    -.0455609    .0478863
       _cons |    1.91909   .0362372    52.96   0.000     1.847898    1.990282
------------------------------------------------------------------------------

.         * Or on majority status or chair positions
.         reg r_eff3 les chair subchr majority seniority if efinfo==0

      Source |       SS           df       MS      Number of obs   =       514
-------------+----------------------------------   F(5, 508)       =      5.44
       Model |  10.2501278         5  2.05002556   Prob > F        =    0.0001
    Residual |  191.479444       508   .37692804   R-squared       =    0.0508
-------------+----------------------------------   Adj R-squared   =    0.0415
       Total |  201.729572       513  .393235033   Root MSE        =    .61394

------------------------------------------------------------------------------
      r_eff3 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         les |  -.0004324   .0273728    -0.02   0.987    -.0542104    .0533455
       chair |    .130018   .1719469     0.76   0.450    -.2077966    .4678326
      subchr |   -.034304   .0772909    -0.44   0.657    -.1861532    .1175452
    majority |  -.1276463   .0690607    -1.85   0.065    -.2633261    .0080334
   seniority |   .0226993   .0067596     3.36   0.001     .0094191    .0359795
       _cons |   1.876306   .0619269    30.30   0.000     1.754642    1.997971
------------------------------------------------------------------------------

.         * Although somewhat on seniority
.         reg r_eff3 seniority if efinfo==0

      Source |       SS           df       MS      Number of obs   =       514
-------------+----------------------------------   F(1, 512)       =     20.80
       Model |  7.87375053         1  7.87375053   Prob > F        =    0.0000
    Residual |  193.855821       512  .378624651   R-squared       =    0.0390
-------------+----------------------------------   Adj R-squared   =    0.0372
       Total |  201.729572       513  .393235033   Root MSE        =    .61532

------------------------------------------------------------------------------
      r_eff3 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
   seniority |   .0279319   .0061251     4.56   0.000     .0158984    .0399653
       _cons |   1.773836   .0420386    42.20   0.000     1.691246    1.856425
------------------------------------------------------------------------------

. 
. *************************************
. ** Figure 4: Main Treatment Effect **
. *************************************
. 
. ** Results in Paper
.         reg approve efinfo if effective==1

      Source |       SS           df       MS      Number of obs   =       249
-------------+----------------------------------   F(1, 247)       =      2.52
       Model |  .533753706         1  .533753706   Prob > F        =    0.1135
    Residual |  52.2694591       247  .211617244   R-squared       =    0.0101
-------------+----------------------------------   Adj R-squared   =    0.0061
       Total |  52.8032129       248  .212916181   Root MSE        =    .46002

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |   .0926165   .0583168     1.59   0.114    -.0222451    .2074781
       _cons |    .647541   .0416481    15.55   0.000     .5655102    .7295718
------------------------------------------------------------------------------

.         est store Effective

. 
.         reg approve efinfo if avgeffective==1

      Source |       SS           df       MS      Number of obs   =       366
-------------+----------------------------------   F(1, 364)       =      0.03
       Model |  .008252541         1  .008252541   Prob > F        =    0.8518
    Residual |  85.9589606       364  .236150991   R-squared       =    0.0001
-------------+----------------------------------   Adj R-squared   =   -0.0027
       Total |  85.9672131       365  .235526611   Root MSE        =    .48595

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |  -.0094982   .0508092    -0.19   0.852    -.1094147    .0904183
       _cons |   .6277778   .0362208    17.33   0.000     .5565494    .6990062
------------------------------------------------------------------------------

.         est store Avg_Effective

. 
.         reg approve efinfo if ineffective==1 

      Source |       SS           df       MS      Number of obs   =       434
-------------+----------------------------------   F(1, 432)       =     37.03
       Model |  8.56628317         1  8.56628317   Prob > F        =    0.0000
    Residual |  99.9245002       432  .231306714   R-squared       =    0.0790
-------------+----------------------------------   Adj R-squared   =    0.0768
       Total |  108.490783       433  .250556082   Root MSE        =    .48094

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |  -.2810316   .0461799    -6.09   0.000    -.3717968   -.1902663
       _cons |   .6384977   .0329537    19.38   0.000     .5737281    .7032672
------------------------------------------------------------------------------

.         est store Ineffective

. 
.         coefplot (Effective, drop(_cons) mcol(green) label(Highly Effective)) (Avg_Effective, drop(_cons) 
> mcol(black) label(Average)) (Ineffective, drop(_cons) mcol(red)),  ylabel("") title("CCES, Approval of Law
> maker, Treatment Effects", size(medium)) legend(row(1) order(6 4 2)) xlabel (-.4 "-.4" -.2 "-.2" 0 "0" .2 
> ".2" .4 ".4" -.3 `"" " "Lower Approval""'  .3`"" "  "Higher Approval""', noticks) xtitle(Effect of Effecti
> veness Information on Approval of their Lawmaker)       

.                 graph save "MainTreatment_CCES.gph", replace
(file MainTreatment_CCES.gph not found)
file MainTreatment_CCES.gph saved

.                 
. ** Appendix
.         logit approve efinfo if effective==1

Iteration 0:   log likelihood = -153.19059  
Iteration 1:   log likelihood = -151.93216  
Iteration 2:   log likelihood = -151.92988  
Iteration 3:   log likelihood = -151.92988  

Logistic regression                                     Number of obs =    249
                                                        LR chi2(1)    =   2.52
                                                        Prob > chi2   = 0.1123
Log likelihood = -151.92988                             Pseudo R2     = 0.0082

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |   .4385395    .277228     1.58   0.114    -.1048174    .9818964
       _cons |   .6082477     .18951     3.21   0.001      .236815    .9796805
------------------------------------------------------------------------------

.         margins, at(efinfo=(0(1)1)) post

Adjusted predictions                                       Number of obs = 249
Model VCE: OIM

Expression: Pr(approve), predict()
1._at: efinfo = 0
2._at: efinfo = 1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         _at |
          1  |    .647541   .0432522    14.97   0.000     .5627683    .7323137
          2  |   .7401575   .0389148    19.02   0.000     .6638858    .8164292
------------------------------------------------------------------------------

.         est store Effective

. 
.         logit approve efinfo if avgeffective==1

Iteration 0:   log likelihood = -242.51199  
Iteration 1:   log likelihood = -242.49442  
Iteration 2:   log likelihood = -242.49442  

Logistic regression                                     Number of obs =    366
                                                        LR chi2(1)    =   0.04
                                                        Prob > chi2   = 0.8513
Log likelihood = -242.49442                             Pseudo R2     = 0.0001

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |  -.0404429   .2157663    -0.19   0.851    -.4633371    .3824512
       _cons |   .5226952   .1541912     3.39   0.001      .220486    .8249044
------------------------------------------------------------------------------

.         margins, at(efinfo=(0(1)1)) post

Adjusted predictions                                       Number of obs = 366
Model VCE: OIM

Expression: Pr(approve), predict()
1._at: efinfo = 0
2._at: efinfo = 1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .6277778   .0360303    17.42   0.000     .5571597    .6983959
          2  |   .6182796   .0356212    17.36   0.000     .5484633    .6880959
------------------------------------------------------------------------------

.         est store Avg_Effective

. 
.         logit approve efinfo if ineffective==1 

Iteration 0:   log likelihood = -300.80744  
Iteration 1:   log likelihood = -283.45335  
Iteration 2:   log likelihood = -283.44128  
Iteration 3:   log likelihood = -283.44128  

Logistic regression                                     Number of obs =    434
                                                        LR chi2(1)    =  34.73
                                                        Prob > chi2   = 0.0000
Log likelihood = -283.44128                             Pseudo R2     = 0.0577

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |  -1.155229   .2001011    -5.77   0.000     -1.54742   -.7630378
       _cons |   .5688495   .1426182     3.99   0.000     .2893229     .848376
------------------------------------------------------------------------------

.         margins, at(efinfo=(0(1)1)) post

Adjusted predictions                                       Number of obs = 434
Model VCE: OIM

Expression: Pr(approve), predict()
1._at: efinfo = 0
2._at: efinfo = 1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .6384977   .0329189    19.40   0.000     .5739778    .7030175
          2  |   .3574661   .0322381    11.09   0.000     .2942806    .4206516
------------------------------------------------------------------------------

.         est store Ineffective

. 
.         coefplot (Effective, mcol(green) label(Highly Effective)) (Avg_Effective, mcol(black) label(Averag
> e)) (Ineffective, mcol(red)),  ylabel(`=1' "Control" `=2' "Treated") title("CCES, Approval of Lawmaker", s
> ize(medium)) legend(row(1) order(6 4 2))

.                 graph save "MainTreatment_CCES_Appendix.gph", replace
(file MainTreatment_CCES_Appendix.gph not found)
file MainTreatment_CCES_Appendix.gph saved

. 
. ** Same as above, but weighted
.         logit approve efinfo [pw=weight] if effective==1

Iteration 0:   log pseudolikelihood = -146.27068  
Iteration 1:   log pseudolikelihood = -143.85243  
Iteration 2:   log pseudolikelihood = -143.84472  
Iteration 3:   log pseudolikelihood = -143.84472  

Logistic regression                                     Number of obs =    249
                                                        Wald chi2(1)  =   3.12
                                                        Prob > chi2   = 0.0771
Log pseudolikelihood = -143.84472                       Pseudo R2     = 0.0166

------------------------------------------------------------------------------
             |               Robust
     approve | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |   .6233841   .3526635     1.77   0.077    -.0678236    1.314592
       _cons |   .4628737   .2594533     1.78   0.074    -.0456454    .9713928
------------------------------------------------------------------------------

.         margins, at(efinfo=(0(1)1)) post

Adjusted predictions                                       Number of obs = 249
Model VCE: Robust

Expression: Pr(approve), predict()
1._at: efinfo = 0
2._at: efinfo = 1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .6136957   .0615094     9.98   0.000     .4931394     .734252
          2  |   .7476764    .045063    16.59   0.000     .6593545    .8359983
------------------------------------------------------------------------------

.         est store Effective

. 
.         logit approve efinfo [pw=weight] if avgeffective==1

Iteration 0:   log pseudolikelihood = -251.46328  
Iteration 1:   log pseudolikelihood = -251.41224  
Iteration 2:   log pseudolikelihood = -251.41224  

Logistic regression                                     Number of obs =    366
                                                        Wald chi2(1)  =   0.05
                                                        Prob > chi2   = 0.8281
Log pseudolikelihood = -251.41224                       Pseudo R2     = 0.0002

------------------------------------------------------------------------------
             |               Robust
     approve | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |  -.0679649   .3129829    -0.22   0.828       -.6814    .5454703
       _cons |    .483803    .218083     2.22   0.027     .0563681    .9112378
------------------------------------------------------------------------------

.         margins, at(efinfo=(0(1)1)) post

Adjusted predictions                                       Number of obs = 366
Model VCE: Robust

Expression: Pr(approve), predict()
1._at: efinfo = 0
2._at: efinfo = 1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .6186455   .0514509    12.02   0.000     .5178037    .7194873
          2  |   .6024869   .0537658    11.21   0.000     .4971079    .7078659
------------------------------------------------------------------------------

.         est store Avg_Effective

. 
.         logit approve efinfo  [pw=weight] if ineffective==1 

Iteration 0:   log pseudolikelihood = -309.93262  
Iteration 1:   log pseudolikelihood = -289.13194  
Iteration 2:   log pseudolikelihood = -289.09742  
Iteration 3:   log pseudolikelihood = -289.09742  

Logistic regression                                     Number of obs =    434
                                                        Wald chi2(1)  =  20.11
                                                        Prob > chi2   = 0.0000
Log pseudolikelihood = -289.09742                       Pseudo R2     = 0.0672

------------------------------------------------------------------------------
             |               Robust
     approve | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |  -1.256452   .2801533    -4.48   0.000    -1.805543   -.7073621
       _cons |    .446525   .2033301     2.20   0.028     .0480053    .8450447
------------------------------------------------------------------------------

.         margins, at(efinfo=(0(1)1)) post

Adjusted predictions                                       Number of obs = 434
Model VCE: Robust

Expression: Pr(approve), predict()
1._at: efinfo = 0
2._at: efinfo = 1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .6098127   .0483806    12.60   0.000     .5149884    .7046369
          2  |    .307906   .0410696     7.50   0.000     .2274111    .3884008
------------------------------------------------------------------------------

.         est store Ineffective

. 
.         coefplot (Effective, mcol(green) label(Highly Effective)) (Avg_Effective, mcol(black) label(Averag
> e)) (Ineffective, mcol(red)),  ylabel(`=1' "Control" `=2' "Treated") title("Weighted CCES, Approval of Law
> maker") legend(row(1) order(6 4 2)) 

.                 graph save "MainTreatment_CCES_weighted.gph", replace
(file MainTreatment_CCES_weighted.gph not found)
file MainTreatment_CCES_weighted.gph saved

. 
.                 
. ******************************************************************
. ** Figure 5: Heterogeneous Treatment Effects, by copartisanship **
. ******************************************************************
. 
. ** Results in Paper
.         reg approve efinfo if effective==1 & copartisan==1

      Source |       SS           df       MS      Number of obs   =       128
-------------+----------------------------------   F(1, 126)       =      1.74
       Model |  .169753181         1  .169753181   Prob > F        =    0.1896
    Residual |  12.2989968       126  .097611086   R-squared       =    0.0136
-------------+----------------------------------   Adj R-squared   =    0.0058
       Total |    12.46875       127  .098179134   Root MSE        =    .31243

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |   .0729141   .0552907     1.32   0.190    -.0365046    .1823328
       _cons |    .852459   .0400023    21.31   0.000     .7732957    .9316223
------------------------------------------------------------------------------

.                 est store E1

.         reg approve efinfo if effective==1 & copartisan==0

      Source |       SS           df       MS      Number of obs   =        76
-------------+----------------------------------   F(1, 74)        =      0.23
       Model |  .052631579         1  .052631579   Prob > F        =    0.6341
    Residual |  17.0526316        74  .230440967   R-squared       =    0.0031
-------------+----------------------------------   Adj R-squared   =   -0.0104
       Total |  17.1052632        75  .228070175   Root MSE        =    .48004

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |   .0526316   .1101293     0.48   0.634    -.1668059    .2720691
       _cons |   .3157895   .0778732     4.06   0.000     .1606237    .4709552
------------------------------------------------------------------------------

.                 est store E2

.                 
.         reg approve efinfo if avgeffective==1 & copartisan==1

      Source |       SS           df       MS      Number of obs   =       161
-------------+----------------------------------   F(1, 159)       =      2.84
       Model |  .332837322         1  .332837322   Prob > F        =    0.0941
    Residual |  18.6609515       159  .117364475   R-squared       =    0.0175
-------------+----------------------------------   Adj R-squared   =    0.0113
       Total |  18.9937888       160   .11871118   Root MSE        =    .34258

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |  -.0909793    .054025    -1.68   0.094    -.1976785    .0157199
       _cons |   .9102564   .0387901    23.47   0.000     .8336461    .9868667
------------------------------------------------------------------------------

.                 est store A1

.         reg approve efinfo if avgeffective==1 & copartisan==0

      Source |       SS           df       MS      Number of obs   =       117
-------------+----------------------------------   F(1, 115)       =      1.46
       Model |  .342083842         1  .342083842   Prob > F        =    0.2286
    Residual |  26.8544974       115  .233517368   R-squared       =    0.0126
-------------+----------------------------------   Adj R-squared   =    0.0040
       Total |  27.1965812       116  .234453286   Root MSE        =    .48324

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |   .1084656    .089616     1.21   0.229    -.0690464    .2859776
       _cons |   .3174603   .0608821     5.21   0.000     .1968647     .438056
------------------------------------------------------------------------------

.                 est store A2

. 
.         reg approve efinfo if ineffective==1 & copartisan==1

      Source |       SS           df       MS      Number of obs   =       229
-------------+----------------------------------   F(1, 227)       =     52.95
       Model |  9.19271018         1  9.19271018   Prob > F        =    0.0000
    Residual |  39.4099099       227  .173611938   R-squared       =    0.1891
-------------+----------------------------------   Adj R-squared   =    0.1856
       Total |  48.6026201       228  .213169386   Root MSE        =    .41667

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |  -.4009009   .0550941    -7.28   0.000    -.5094621   -.2923397
       _cons |   .9009009   .0395483    22.78   0.000     .8229721    .9788297
------------------------------------------------------------------------------

.                 est store I1

.         reg approve efinfo if ineffective==1 & copartisan==0

      Source |       SS           df       MS      Number of obs   =       130
-------------+----------------------------------   F(1, 128)       =      8.59
       Model |   1.4507326         1   1.4507326   Prob > F        =    0.0040
    Residual |  21.6261905       128  .168954613   R-squared       =    0.0629
-------------+----------------------------------   Adj R-squared   =    0.0555
       Total |  23.0769231       129  .178890877   Root MSE        =    .41104

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |  -.2119048   .0723156    -2.93   0.004    -.3549936    -.068816
       _cons |   .3285714   .0491288     6.69   0.000     .2313617    .4257811
------------------------------------------------------------------------------

.                 est store I2

. 
.         coefplot (E1, drop(_cons) mcol(green) msymbol(O) label(Highly Effective)) (A1, drop(_cons) mcol(bl
> ack) msymbol(D)  label(Average)) (I1, drop(_cons) mcol(red) msymbol(S)  label(Ineffective))   (E2, drop(_c
> ons) mcol(green) msymbol(O) label(Highly Effective)) (A2, drop(_cons) mcol(black) msymbol(D) label(Average
> )) (I2, drop(_cons) mcol(red) msymbol(S) label(Ineffective)),  ylabel("")  title("CCES Approval, by Partis
> an Alignment")  ylabel(.8 "Copartisan" 1.2 "Outparty") legend(row(1) order(6 4 2)) xlabel (-.4 "-.4" -.2 "
> -.2" 0 "0" .2 ".2" .4 ".4" -.3 `"" " "Lower Approval""'  .3`"" "  "Higher Approval""', noticks) xtitle(Eff
> ect of Effectiveness Information on Approval of their Lawmaker)

.                 graph save "Partisanship_CCES.gph", replace
(file Partisanship_CCES.gph not found)
file Partisanship_CCES.gph saved

. 
. ** Appendix
.         logit approve copartisan##efinfo if effective==1 

Iteration 0:   log likelihood = -126.89802  
Iteration 1:   log likelihood = -93.467784  
Iteration 2:   log likelihood = -92.032415  
Iteration 3:   log likelihood = -92.015568  
Iteration 4:   log likelihood = -92.015559  

Logistic regression                                     Number of obs =    204
                                                        LR chi2(3)    =  69.76
                                                        Prob > chi2   = 0.0000
Log likelihood = -92.015559                             Pseudo R2     = 0.2749

-----------------------------------------------------------------------------------
          approve | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
------------------+----------------------------------------------------------------
     1.copartisan |   2.527209   .5021322     5.03   0.000     1.543048     3.51137
         1.efinfo |   .2341934   .4846546     0.48   0.629    -.7157122    1.184099
                  |
copartisan#efinfo |
             1 1  |   .5294824   .7624702     0.69   0.487    -.9649317    2.023896
                  |
            _cons |  -.7731899   .3489912    -2.22   0.027      -1.4572   -.0891797
-----------------------------------------------------------------------------------

.                 margins, at(efinfo=(0(1)1) copartisan=(0(1)1)) post

Adjusted predictions                                       Number of obs = 204
Model VCE: OIM

Expression: Pr(approve), predict()
1._at: copartisan = 0
       efinfo     = 0
2._at: copartisan = 0
       efinfo     = 1
3._at: copartisan = 1
       efinfo     = 0
4._at: copartisan = 1
       efinfo     = 1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .3157895   .0754053     4.19   0.000     .1679978    .4635812
          2  |   .3684211   .0782518     4.71   0.000     .2150504    .5217917
          3  |    .852459   .0454076    18.77   0.000     .7634618    .9414562
          4  |    .925373   .0321047    28.82   0.000     .8624489    .9882971
------------------------------------------------------------------------------

.                 est store Effective

.         
.         logit approve copartisan##efinfo if avgeffective==1

Iteration 0:   log likelihood = -179.17203  
Iteration 1:   log likelihood = -140.29768  
Iteration 2:   log likelihood = -138.98266  
Iteration 3:   log likelihood = -138.97455  
Iteration 4:   log likelihood = -138.97455  

Logistic regression                                     Number of obs =    278
                                                        LR chi2(3)    =  80.39
                                                        Prob > chi2   = 0.0000
Log likelihood = -138.97455                             Pseudo R2     = 0.2244

-----------------------------------------------------------------------------------
          approve | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
------------------+----------------------------------------------------------------
     1.copartisan |   3.082237    .479789     6.42   0.000     2.141868    4.022606
         1.efinfo |   .4669749    .385995     1.21   0.226    -.2895614    1.223511
                  |
copartisan#efinfo |
             1 1  |  -1.272287   .6223394    -2.04   0.041     -2.49205   -.0525241
                  |
            _cons |  -.7654678   .2706581    -2.83   0.005    -1.295948   -.2349877
-----------------------------------------------------------------------------------

.                 margins, at(efinfo=(0(1)1) copartisan=(0(1)1)) post

Adjusted predictions                                       Number of obs = 278
Model VCE: OIM

Expression: Pr(approve), predict()
1._at: copartisan = 0
       efinfo     = 0
2._at: copartisan = 0
       efinfo     = 1
3._at: copartisan = 1
       efinfo     = 0
4._at: copartisan = 1
       efinfo     = 1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .3174603    .058646     5.41   0.000     .2025163    .4324044
          2  |   .4259259   .0672906     6.33   0.000     .2940389     .557813
          3  |   .9102564   .0323621    28.13   0.000     .8468279    .9736849
          4  |   .8192771    .042236    19.40   0.000      .736496    .9020582
------------------------------------------------------------------------------

.                 est store Avg_Effective

. 
.         logit approve copartisan##efinfo if ineffective==1 

Iteration 0:   log likelihood = -248.33682  
Iteration 1:   log likelihood = -184.21904  
Iteration 2:   log likelihood = -183.59267  
Iteration 3:   log likelihood = -183.59055  
Iteration 4:   log likelihood = -183.59055  

Logistic regression                                     Number of obs =    359
                                                        LR chi2(3)    = 129.49
                                                        Prob > chi2   = 0.0000
Log likelihood = -183.59055                             Pseudo R2     = 0.2607

-----------------------------------------------------------------------------------
          approve | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
------------------+----------------------------------------------------------------
     1.copartisan |   2.921928   .4070184     7.18   0.000     2.124187     3.71967
         1.efinfo |  -1.309728   .4758991    -2.75   0.006    -2.242473   -.3769833
                  |
copartisan#efinfo |
             1 1  |  -.8975465   .6010718    -1.49   0.135    -2.075626    .2805326
                  |
            _cons |  -.7146534   .2544698    -2.81   0.005    -1.213405   -.2159018
-----------------------------------------------------------------------------------

.                 margins, at(efinfo=(0(1)1) copartisan=(0(1)1)) post

Adjusted predictions                                       Number of obs = 359
Model VCE: OIM

Expression: Pr(approve), predict()
1._at: copartisan = 0
       efinfo     = 0
2._at: copartisan = 0
       efinfo     = 1
3._at: copartisan = 1
       efinfo     = 0
4._at: copartisan = 1
       efinfo     = 1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .3285714   .0561391     5.85   0.000     .2185407    .4386021
          2  |   .1166667   .0414438     2.82   0.005     .0354382    .1978951
          3  |   .9009009   .0283604    31.77   0.000     .8453156    .9564862
          4  |         .5   .0460287    10.86   0.000     .4097853    .5902147
------------------------------------------------------------------------------

.                 est store Ineffective

. 
.         coefplot (Effective, mcol(green) label(Highly Effective)) (Avg_Effective, mcol(black) label(Averag
> e)) (Ineffective, mcol(red)),  ylabel(`=1' "Outparty, Control" `=2' "Outparty, Treated" `=3' "Copartisan, 
> Control" `=4' "Copartisan, Treated")  title("CCES Approval, by Partisan Alignment") legend(row(1) order(6 
> 4 2))

.                 graph save "Partisanship_CCES_Appendix.gph", replace
(file Partisanship_CCES_Appendix.gph not found)
file Partisanship_CCES_Appendix.gph saved

. 
.                 
. ** Note: if run this separately for Democrats and Republicans, get about the same patterns, but with more 
> noise         
. 
. ** Same as above, but weighted
.         logit approve copartisan##efinfo [pw=weight] if effective==1 

Iteration 0:   log pseudolikelihood = -118.71371  
Iteration 1:   log pseudolikelihood = -81.657626  
Iteration 2:   log pseudolikelihood =  -80.19347  
Iteration 3:   log pseudolikelihood = -80.153848  
Iteration 4:   log pseudolikelihood = -80.153794  
Iteration 5:   log pseudolikelihood = -80.153794  

Logistic regression                                     Number of obs =    204
                                                        Wald chi2(3)  =  49.97
                                                        Prob > chi2   = 0.0000
Log pseudolikelihood = -80.153794                       Pseudo R2     = 0.3248

-----------------------------------------------------------------------------------
                  |               Robust
          approve | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
------------------+----------------------------------------------------------------
     1.copartisan |   3.043266   .6483307     4.69   0.000     1.772561    4.313971
         1.efinfo |   .8102898   .6258349     1.29   0.195    -.4163241    2.036904
                  |
copartisan#efinfo |
             1 1  |   .2305224   .9058241     0.25   0.799     -1.54486    2.005905
                  |
            _cons |  -1.242282   .4786287    -2.60   0.009    -2.180377   -.3041868
-----------------------------------------------------------------------------------

.                 margins, at(efinfo=(0(1)1) copartisan=(0(1)1)) post

Adjusted predictions                                       Number of obs = 204
Model VCE: Robust

Expression: Pr(approve), predict()
1._at: copartisan = 0
       efinfo     = 0
2._at: copartisan = 0
       efinfo     = 1
3._at: copartisan = 1
       efinfo     = 0
4._at: copartisan = 1
       efinfo     = 1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .2240391   .0832075     2.69   0.007     .0609554    .3871227
          2  |   .3936507   .0962438     4.09   0.000     .2050164    .5822851
          3  |   .8582687    .053197    16.13   0.000     .7540046    .9625328
          4  |   .9448931   .0253812    37.23   0.000     .8951468    .9946394
------------------------------------------------------------------------------

.                 est store Effective

.         
.         logit approve copartisan##efinfo [pw=weight] if avgeffective==1

Iteration 0:   log pseudolikelihood = -188.01312  
Iteration 1:   log pseudolikelihood = -144.04041  
Iteration 2:   log pseudolikelihood = -143.14236  
Iteration 3:   log pseudolikelihood =  -143.1322  
Iteration 4:   log pseudolikelihood =  -143.1322  

Logistic regression                                     Number of obs =    278
                                                        Wald chi2(3)  =  48.33
                                                        Prob > chi2   = 0.0000
Log pseudolikelihood = -143.1322                        Pseudo R2     = 0.2387

-----------------------------------------------------------------------------------
                  |               Robust
          approve | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
------------------+----------------------------------------------------------------
     1.copartisan |   3.342256   .5492905     6.08   0.000     2.265667    4.418846
         1.efinfo |   .3919152   .5334654     0.73   0.463    -.6536578    1.437488
                  |
copartisan#efinfo |
             1 1  |  -1.398913   .7993102    -1.75   0.080    -2.965532    .1677062
                  |
            _cons |   -1.02582   .3584994    -2.86   0.004    -1.728466   -.3231742
-----------------------------------------------------------------------------------

.                 margins, at(efinfo=(0(1)1) copartisan=(0(1)1)) post

Adjusted predictions                                       Number of obs = 278
Model VCE: Robust

Expression: Pr(approve), predict()
1._at: copartisan = 0
       efinfo     = 0
2._at: copartisan = 0
       efinfo     = 1
3._at: copartisan = 1
       efinfo     = 0
4._at: copartisan = 1
       efinfo     = 1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .2638953   .0696401     3.79   0.000     .1274031    .4003874
          2  |   .3466256   .0894692     3.87   0.000     .1712693     .521982
          3  |   .9102292   .0340062    26.77   0.000     .8435783      .97688
          4  |   .7874192   .0712369    11.05   0.000     .6477973     .927041
------------------------------------------------------------------------------

.                 est store Avg_Effective

. 
.         logit approve copartisan##efinfo [pw=weight] if ineffective==1 

Iteration 0:   log pseudolikelihood = -236.91937  
Iteration 1:   log pseudolikelihood = -171.89917  
Iteration 2:   log pseudolikelihood = -171.32361  
Iteration 3:   log pseudolikelihood = -171.32273  
Iteration 4:   log pseudolikelihood = -171.32273  

Logistic regression                                     Number of obs =    359
                                                        Wald chi2(3)  =  60.25
                                                        Prob > chi2   = 0.0000
Log pseudolikelihood = -171.32273                       Pseudo R2     = 0.2769

-----------------------------------------------------------------------------------
                  |               Robust
          approve | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
------------------+----------------------------------------------------------------
     1.copartisan |   2.753396   .5522747     4.99   0.000     1.670958    3.835835
         1.efinfo |   -1.86566   .5987476    -3.12   0.002    -3.039184   -.6921366
                  |
copartisan#efinfo |
             1 1  |  -.3954352   .7600296    -0.52   0.603    -1.885066    1.094195
                  |
            _cons |  -.5266516   .3832203    -1.37   0.169    -1.277749    .2244463
-----------------------------------------------------------------------------------

.                 margins, at(efinfo=(0(1)1) copartisan=(0(1)1)) post

Adjusted predictions                                       Number of obs = 359
Model VCE: Robust

Expression: Pr(approve), predict()
1._at: copartisan = 0
       efinfo     = 0
2._at: copartisan = 0
       efinfo     = 1
3._at: copartisan = 1
       efinfo     = 0
4._at: copartisan = 1
       efinfo     = 1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .3712982   .0894573     4.15   0.000      .195965    .5466314
          2  |   .0837608   .0353061     2.37   0.018     .0145622    .1529595
          3  |   .9026256   .0349532    25.82   0.000     .8341187    .9711325
          4  |   .4914131   .0617255     7.96   0.000     .3704333    .6123929
------------------------------------------------------------------------------

.                 est store Ineffective

. 
.         coefplot (Effective, mcol(green) label(Highly Effective)) (Avg_Effective, mcol(black) label(Averag
> e)) (Ineffective, mcol(red)),  ylabel(`=1' "Outparty, Control" `=2' "Outparty, Treated" `=3' "Copartisan, 
> Control" `=4' "Copartisan, Treated")  title("Weighted CCES Approval, by Partisan Alignment") legend(row(1)
>  order(6 4 2))

.                 graph save "Partisanship_CCES_weighted.gph", replace
(file Partisanship_CCES_weighted.gph not found)
file Partisanship_CCES_weighted.gph saved

. 
. 
. ************************************************************
. ** Figure 6: Heterogenenous treatment effects by ideology **
. ************************************************************
. 
. *Create ideological extremism indicator
. gen extreme=0

. replace extreme=1 if CC16_340a==1 | CC16_340a==2 | CC16_340a==6 | CC16_340a==7
(506 real changes made)

. label var extreme "1 = Self identified as Very Liberal, Liberal, Conservative, or Very Conservative"

. 
. ** Results in Paper
.         reg approve efinfo if effective==1 & extreme==1

      Source |       SS           df       MS      Number of obs   =       123
-------------+----------------------------------   F(1, 121)       =      0.13
       Model |  .024611661         1  .024611661   Prob > F        =    0.7233
    Residual |  23.6501851       121  .195456075   R-squared       =    0.0010
-------------+----------------------------------   Adj R-squared   =   -0.0072
       Total |  23.6747967       122  .194055711   Root MSE        =     .4421

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |  -.0282919    .079729    -0.35   0.723    -.1861365    .1295527
       _cons |   .7540984   .0566056    13.32   0.000     .6420326    .8661641
------------------------------------------------------------------------------

.                 est store E1

.         reg approve efinfo if effective==1 & extreme==0

      Source |       SS           df       MS      Number of obs   =       126
-------------+----------------------------------   F(1, 124)       =      6.50
       Model |  1.42584119         1  1.42584119   Prob > F        =    0.0120
    Residual |  27.2090794       124   .21942806   R-squared       =    0.0498
-------------+----------------------------------   Adj R-squared   =    0.0421
       Total |  28.6349206       125  .229079365   Root MSE        =    .46843

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |   .2128625   .0835045     2.55   0.012     .0475838    .3781413
       _cons |   .5409836   .0599765     9.02   0.000     .4222733    .6596939
------------------------------------------------------------------------------

.                 est store E2

.                 
.         reg approve efinfo if avgeffective==1 & extreme==1

      Source |       SS           df       MS      Number of obs   =       170
-------------+----------------------------------   F(1, 168)       =      0.09
       Model |  .020868658         1  .020868658   Prob > F        =    0.7641
    Residual |  38.8026608       168  .230968219   R-squared       =    0.0005
-------------+----------------------------------   Adj R-squared   =   -0.0054
       Total |  38.8235294       169  .229725026   Root MSE        =    .48059

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |  -.0221729   .0737653    -0.30   0.764    -.1677994    .1234535
       _cons |   .6585366   .0530725    12.41   0.000     .5537617    .7633114
------------------------------------------------------------------------------

.                 est store A1

.         reg approve efinfo if avgeffective==1 & extreme==0

      Source |       SS           df       MS      Number of obs   =       196
-------------+----------------------------------   F(1, 194)       =      0.00
       Model |           0         1           0   Prob > F        =    1.0000
    Residual |  46.9591837       194  .242057648   R-squared       =    0.0000
-------------+----------------------------------   Adj R-squared   =   -0.0052
       Total |  46.9591837       195  .240816327   Root MSE        =    .49199

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |   4.53e-18   .0702848     0.00   1.000    -.1386204    .1386204
       _cons |   .6020408   .0496989    12.11   0.000     .5040214    .7000603
------------------------------------------------------------------------------

.                 est store A2

. 
.         reg approve efinfo if ineffective==1 & extreme==1

      Source |       SS           df       MS      Number of obs   =       213
-------------+----------------------------------   F(1, 211)       =     11.57
       Model |   2.7347567         1   2.7347567   Prob > F        =    0.0008
    Residual |  49.8943513       211   .23646612   R-squared       =    0.0520
-------------+----------------------------------   Adj R-squared   =    0.0475
       Total |   52.629108       212  .248250509   Root MSE        =    .48628

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |  -.2267432   .0666744    -3.40   0.001    -.3581765   -.0953098
       _cons |   .6636364   .0463648    14.31   0.000     .5722389    .7550339
------------------------------------------------------------------------------

.                 est store I1

.         reg approve efinfo if ineffective==1 & extreme==0

      Source |       SS           df       MS      Number of obs   =       221
-------------+----------------------------------   F(1, 219)       =     25.90
       Model |  5.75593012         1  5.75593012   Prob > F        =    0.0000
    Residual |  48.6694092       219  .222234745   R-squared       =    0.1058
-------------+----------------------------------   Adj R-squared   =    0.1017
       Total |  54.4253394       220  .247387906   Root MSE        =    .47142

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |  -.3235149   .0635686    -5.09   0.000    -.4487994   -.1982304
       _cons |   .6116505   .0464502    13.17   0.000     .5201039    .7031971
------------------------------------------------------------------------------

.                 est store I2

. 
.         coefplot (E1, drop(_cons) mcol(green) msymbol(O) label(Highly Effective)) (A1, drop(_cons) mcol(bl
> ack) msymbol(D)  label(Average)) (I1, drop(_cons) mcol(red) msymbol(S)  label(Ineffective))   (E2, drop(_c
> ons) mcol(green) msymbol(O) label(Highly Effective)) (A2, drop(_cons) mcol(black) msymbol(D) label(Average
> )) (I2, drop(_cons) mcol(red) msymbol(S) label(Ineffective)),  ylabel("")  title("CCES Approval, by Ideolo
> gical Extermism") ylabel(.8 "Extreme" 1.2 "Moderate") legend(row(1) order(6 4 2)) xlabel (-.4 "-.4" -.2 "-
> .2" 0 "0" .2 ".2" .4 ".4" -.3 `"" " "Lower Approval""'  .3`"" "  "Higher Approval""', noticks) xtitle(Effe
> ct of Effectiveness Information on Approval of their Lawmaker) 

.                 graph save "IdeologicalExtremism_CCES.gph", replace
(file IdeologicalExtremism_CCES.gph not found)
file IdeologicalExtremism_CCES.gph saved

.                 
. ** Appendix
. logit approve extreme##efinfo if effective==1 

Iteration 0:   log likelihood = -153.19059  
Iteration 1:   log likelihood = -148.83239  
Iteration 2:   log likelihood = -148.79433  
Iteration 3:   log likelihood = -148.79433  

Logistic regression                                     Number of obs =    249
                                                        LR chi2(3)    =   8.79
                                                        Prob > chi2   = 0.0322
Log likelihood = -148.79433                             Pseudo R2     = 0.0287

--------------------------------------------------------------------------------
       approve | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
---------------+----------------------------------------------------------------
     1.extreme |   .9562881   .3929671     2.43   0.015     .1860869    1.726489
      1.efinfo |   .9549285   .3859086     2.47   0.013     .1985615    1.711296
               |
extreme#efinfo |
          1 1  |  -1.102071   .5642491    -1.95   0.051    -2.207978    .0038373
               |
         _cons |   .1643031   .2569384     0.64   0.523    -.3392869     .667893
--------------------------------------------------------------------------------

.                 margins, at(efinfo=(0(1)1) extreme=(0(1)1)) post

Adjusted predictions                                       Number of obs = 249
Model VCE: OIM

Expression: Pr(approve), predict()
1._at: extreme = 0
       efinfo  = 0
2._at: extreme = 0
       efinfo  = 1
3._at: extreme = 1
       efinfo  = 0
4._at: extreme = 1
       efinfo  = 1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .5409836    .063803     8.48   0.000      .415932    .6660352
          2  |   .7538462   .0534303    14.11   0.000     .6491246    .8585677
          3  |   .7540984   .0551353    13.68   0.000     .6460351    .8621616
          4  |   .7258065   .0566556    12.81   0.000     .6147634    .8368495
------------------------------------------------------------------------------

.                 est store Effective

.         
.         logit approve extreme##efinfo if avgeffective==1

Iteration 0:   log likelihood = -242.51199  
Iteration 1:   log likelihood = -242.07302  
Iteration 2:   log likelihood = -242.07291  
Iteration 3:   log likelihood = -242.07291  

Logistic regression                                     Number of obs =    366
                                                        LR chi2(3)    =   0.88
                                                        Prob > chi2   = 0.8307
Log likelihood = -242.07291                             Pseudo R2     = 0.0018

--------------------------------------------------------------------------------
       approve | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
---------------+----------------------------------------------------------------
     1.extreme |   .2428037   .3111639     0.78   0.435    -.3670663    .8526738
      1.efinfo |   4.54e-15   .2918567     0.00   1.000    -.5720287    .5720287
               |
extreme#efinfo |
          1 1  |  -.0971637   .4341892    -0.22   0.823     -.948159    .7538315
               |
         _cons |   .4139758   .2063739     2.01   0.045     .0094904    .8184612
--------------------------------------------------------------------------------

.                 margins, at(efinfo=(0(1)1) extreme=(0(1)1)) post

Adjusted predictions                                       Number of obs = 366
Model VCE: OIM

Expression: Pr(approve), predict()
1._at: extreme = 0
       efinfo  = 0
2._at: extreme = 0
       efinfo  = 1
3._at: extreme = 1
       efinfo  = 0
4._at: extreme = 1
       efinfo  = 1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .6020408   .0494446    12.18   0.000     .5051311    .6989505
          2  |   .6020408   .0494446    12.18   0.000     .5051311    .6989505
          3  |   .6585366   .0523667    12.58   0.000     .5558998    .7611734
          4  |   .6363636   .0512796    12.41   0.000     .5358574    .7368699
------------------------------------------------------------------------------

.                 est store Avg_Effective

. 
.         logit approve extreme##efinfo if ineffective==1 

Iteration 0:   log likelihood = -300.80744  
Iteration 1:   log likelihood = -280.52517  
Iteration 2:   log likelihood = -280.47726  
Iteration 3:   log likelihood = -280.47725  

Logistic regression                                     Number of obs =    434
                                                        LR chi2(3)    =  40.66
                                                        Prob > chi2   = 0.0000
Log likelihood = -280.47725                             Pseudo R2     = 0.0676

--------------------------------------------------------------------------------
       approve | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
---------------+----------------------------------------------------------------
     1.extreme |   .2252863   .2856548     0.79   0.430    -.3345869    .7851594
      1.efinfo |  -1.358711   .2866872    -4.74   0.000    -1.920608   -.7968149
               |
extreme#efinfo |
          1 1  |   .4253894   .4029625     1.06   0.291    -.3644026    1.215181
               |
         _cons |   .4542553   .2021708     2.25   0.025     .0580079    .8505027
--------------------------------------------------------------------------------

.                 margins, at(efinfo=(0(1)1) extreme=(0(1)1)) post

Adjusted predictions                                       Number of obs = 434
Model VCE: OIM

Expression: Pr(approve), predict()
1._at: extreme = 0
       efinfo  = 0
2._at: extreme = 0
       efinfo  = 1
3._at: extreme = 1
       efinfo  = 0
4._at: extreme = 1
       efinfo  = 1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .6116505   .0480225    12.74   0.000     .5175282    .7057728
          2  |   .2881356   .0416923     6.91   0.000     .2064202    .3698511
          3  |   .6636364   .0450478    14.73   0.000     .5753444    .7519284
          4  |   .4368932   .0488725     8.94   0.000     .3411049    .5326815
------------------------------------------------------------------------------

.                 est store Ineffective

. 
.         coefplot (Effective, mcol(green) label (Highly Effective)) (Avg_Effective, mcol(black) label (Aver
> age)) (Ineffective, mcol(red)),  ylabel(`=1' "Moderate, Control" `=2' "Moderate, Treated" `=3' "Extreme, C
> ontrol" `=4' "Extreme, Treated")  title("CCES Approval, by Ideological Extremism") legend(row(1) order(6 4
>  2))

.                 graph save "IdeologicalExtremism_CCES_Appendix.gph", replace
(file IdeologicalExtremism_CCES_Appendix.gph not found)
file IdeologicalExtremism_CCES_Appendix.gph saved

. 
. ** Same as above, but weighted
. 
. logit approve extreme##efinfo [pw=weight] if effective==1 

Iteration 0:   log pseudolikelihood = -146.27068  
Iteration 1:   log pseudolikelihood = -139.71709  
Iteration 2:   log pseudolikelihood =  -139.6291  
Iteration 3:   log pseudolikelihood = -139.62905  
Iteration 4:   log pseudolikelihood = -139.62905  

Logistic regression                                     Number of obs =    249
                                                        Wald chi2(3)  =   8.52
                                                        Prob > chi2   = 0.0363
Log pseudolikelihood = -139.62905                       Pseudo R2     = 0.0454

--------------------------------------------------------------------------------
               |               Robust
       approve | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
---------------+----------------------------------------------------------------
     1.extreme |   .8913816   .5050004     1.77   0.078     -.098401    1.881164
      1.efinfo |   1.431376   .4964173     2.88   0.004     .4584159    2.404336
               |
extreme#efinfo |
          1 1  |  -1.648963   .6875577    -2.40   0.016    -2.996551   -.3013745
               |
         _cons |    .043472   .3651962     0.12   0.905    -.6722993    .7592434
--------------------------------------------------------------------------------

.                 margins, at(efinfo=(0(1)1) extreme=(0(1)1)) post

Adjusted predictions                                       Number of obs = 249
Model VCE: Robust

Expression: Pr(approve), predict()
1._at: extreme = 0
       efinfo  = 0
2._at: extreme = 0
       efinfo  = 1
3._at: extreme = 1
       efinfo  = 0
4._at: extreme = 1
       efinfo  = 1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .5108663   .0912559     5.60   0.000      .332008    .6897246
          2  |   .8137931   .0509528    15.97   0.000     .7139275    .9136587
          3  |   .7180589   .0706134    10.17   0.000     .5796592    .8564587
          4  |   .6720048   .0713027     9.42   0.000      .532254    .8117556
------------------------------------------------------------------------------

.                 est store Effective

.         
.         logit approve extreme##efinfo [pw=weight] if avgeffective==1

Iteration 0:   log pseudolikelihood = -251.46328  
Iteration 1:   log pseudolikelihood = -251.15034  
Iteration 2:   log pseudolikelihood = -251.15024  
Iteration 3:   log pseudolikelihood = -251.15024  

Logistic regression                                     Number of obs =    366
                                                        Wald chi2(3)  =   0.46
                                                        Prob > chi2   = 0.9270
Log pseudolikelihood = -251.15024                       Pseudo R2     = 0.0012

--------------------------------------------------------------------------------
               |               Robust
       approve | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
---------------+----------------------------------------------------------------
     1.extreme |   .2182668    .403241     0.54   0.588     -.572071    1.008605
      1.efinfo |  -.0209928   .4435428    -0.05   0.962    -.8903208    .8483352
               |
extreme#efinfo |
          1 1  |  -.1355776   .5977564    -0.23   0.821    -1.307159    1.036003
               |
         _cons |   .4022856   .3113298     1.29   0.196    -.2079096    1.012481
--------------------------------------------------------------------------------

.                 margins, at(efinfo=(0(1)1) extreme=(0(1)1)) post

Adjusted predictions                                       Number of obs = 366
Model VCE: Robust

Expression: Pr(approve), predict()
1._at: extreme = 0
       efinfo  = 0
2._at: extreme = 0
       efinfo  = 1
3._at: extreme = 1
       efinfo  = 0
4._at: extreme = 1
       efinfo  = 1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .5992367   .0747665     8.01   0.000      .452697    .7457763
          2  |   .5941849    .076177     7.80   0.000     .4448807     .743489
          3  |   .6503442   .0582761    11.16   0.000      .536125    .7645633
          4  |   .6139584   .0730161     8.41   0.000     .4708495    .7570673
------------------------------------------------------------------------------

.                 est store Avg_Effective

. 
.         logit approve extreme##efinfo [pw=weight] if ineffective==1 

Iteration 0:   log pseudolikelihood = -309.93262  
Iteration 1:   log pseudolikelihood = -283.67371  
Iteration 2:   log pseudolikelihood = -283.59378  
Iteration 3:   log pseudolikelihood = -283.59376  

Logistic regression                                     Number of obs =    434
                                                        Wald chi2(3)  =  27.98
                                                        Prob > chi2   = 0.0000
Log pseudolikelihood = -283.59376                       Pseudo R2     = 0.0850

--------------------------------------------------------------------------------
               |               Robust
       approve | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
---------------+----------------------------------------------------------------
     1.extreme |    .855783   .3871357     2.21   0.027      .097011    1.614555
      1.efinfo |  -.9835734   .4109988    -2.39   0.017    -1.789116   -.1780307
               |
extreme#efinfo |
          1 1  |  -.5664018   .5529257    -1.02   0.306    -1.650116    .5173126
               |
         _cons |   .0302519   .2898143     0.10   0.917    -.5377737    .5982775
--------------------------------------------------------------------------------

.                 margins, at(efinfo=(0(1)1) extreme=(0(1)1)) post

Adjusted predictions                                       Number of obs = 434
Model VCE: Robust

Expression: Pr(approve), predict()
1._at: extreme = 0
       efinfo  = 0
2._at: extreme = 0
       efinfo  = 1
3._at: extreme = 1
       efinfo  = 0
4._at: extreme = 1
       efinfo  = 1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .5075624    .072437     7.01   0.000     .3655885    .6495363
          2  |   .2782173   .0585215     4.75   0.000     .1635174    .3929173
          3  |   .7080712   .0530562    13.35   0.000      .604083    .8120595
          4  |    .339855   .0597493     5.69   0.000     .2227485    .4569616
------------------------------------------------------------------------------

.                 est store Ineffective

. 
.         coefplot (Effective, mcol(green) label (Highly Effective)) (Avg_Effective, mcol(black) label (Aver
> age)) (Ineffective, mcol(red)),  ylabel(`=1' "Moderate, Control" `=2' "Moderate, Treated" `=3' "Extreme, C
> ontrol" `=4' "Extreme, Treated")  title("Weighted CCES Approval, by Ideological Extremism") legend(row(1) 
> order(6 4 2))

.                 graph save "IdeologicalExtremism_CCES_weighted.gph", replace
(file IdeologicalExtremism_CCES_weighted.gph not found)
file IdeologicalExtremism_CCES_weighted.gph saved

. 
. 
. *****************
. *****************       
. **   Appendix  **
. **   Figures   **
. *****************
. *****************
.                 
. ** Relationship between vote and actual/perceived Effectiveness
.         bysort effective_numeric: egen perc_vote_true=mean(voteforrep)

.         bysort UVA313: egen perc_vote_perceived=mean(voteforrep)

.         graph bar (mean) perc_vote_true, ylabel(0(.2)1) over(UVA313, relabel(1"Ineffective" 2 "Average" 3 
> "Effective")) ytitle(Probability of Voting for Legislator) title("CCES, Actual Effectiveness") 

.                 graph save vote_actual_cces.gph, replace 
(file vote_actual_cces.gph not found)
file vote_actual_cces.gph saved

.                 
.                 *gen l2=4-UVA313 /*Create this in order to get scale right on graph*/
.         graph bar (mean) perc_vote_perceived, ylabel(0(.2)1) over(l2, relabel(1"Ineffective" 2 "Average" 3
>  "Effective")) ytitle(Probability of Voting for Legislator) title("CCES, Perceived Effectiveness")

.                 graph save vote_perceived_cces.gph, replace 
(file vote_perceived_cces.gph not found)
file vote_perceived_cces.gph saved

. 
. ** Main Treatment Effect - Vote Intention
.         logit voteforrep efinfo if effective==1

Iteration 0:   log likelihood = -173.21479  
Iteration 1:   log likelihood = -172.69582  
Iteration 2:   log likelihood = -172.69582  

Logistic regression                                     Number of obs =    250
                                                        LR chi2(1)    =   1.04
                                                        Prob > chi2   = 0.3083
Log likelihood = -172.69582                             Pseudo R2     = 0.0030

------------------------------------------------------------------------------
  voteforrep | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |   .2581218   .2536286     1.02   0.309    -.2389811    .7552247
       _cons |  -.1793409   .1810594    -0.99   0.322    -.5342109     .175529
------------------------------------------------------------------------------

.         margins, at(efinfo=(0(1)1)) post

Adjusted predictions                                       Number of obs = 250
Model VCE: OIM

Expression: Pr(voteforrep), predict()
1._at: efinfo = 0
2._at: efinfo = 1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .4552846   .0449028    10.14   0.000     .3672766    .5432925
          2  |    .519685   .0443334    11.72   0.000     .4327931     .606577
------------------------------------------------------------------------------

.         est store Effective

. 
.         logit voteforrep efinfo if avgeffective==1

Iteration 0:   log likelihood = -254.46761  
Iteration 1:   log likelihood = -254.36316  
Iteration 2:   log likelihood = -254.36316  

Logistic regression                                     Number of obs =    369
                                                        LR chi2(1)    =   0.21
                                                        Prob > chi2   = 0.6476
Log likelihood = -254.36316                             Pseudo R2     = 0.0004

------------------------------------------------------------------------------
  voteforrep | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |  -.0955247   .2090314    -0.46   0.648    -.5052187    .3141693
       _cons |  -.1203637   .1481121    -0.81   0.416     -.410658    .1699306
------------------------------------------------------------------------------

.         margins, at(efinfo=(0(1)1)) post

Adjusted predictions                                       Number of obs = 369
Model VCE: OIM

Expression: Pr(voteforrep), predict()
1._at: efinfo = 0
2._at: efinfo = 1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .4699454   .0368942    12.74   0.000      .397634    .5422567
          2  |   .4462366   .0364492    12.24   0.000     .3747974    .5176757
------------------------------------------------------------------------------

.         est store Avg_Effective

. 
.         logit voteforrep efinfo if ineffective==1 

Iteration 0:   log likelihood = -299.92255  
Iteration 1:   log likelihood = -299.14082  
Iteration 2:   log likelihood = -299.14078  
Iteration 3:   log likelihood = -299.14078  

Logistic regression                                     Number of obs =    437
                                                        LR chi2(1)    =   1.56
                                                        Prob > chi2   = 0.2111
Log likelihood = -299.14078                             Pseudo R2     = 0.0026

------------------------------------------------------------------------------
  voteforrep | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |  -.2411414   .1930122    -1.25   0.212    -.6194384    .1371555
       _cons |  -.1122673   .1369326    -0.82   0.412    -.3806503    .1561157
------------------------------------------------------------------------------

.         margins, at(efinfo=(0(1)1)) post

Adjusted predictions                                       Number of obs = 437
Model VCE: OIM

Expression: Pr(voteforrep), predict()
1._at: efinfo = 0
2._at: efinfo = 1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .4719626   .0341255    13.83   0.000     .4050778    .5388474
          2  |   .4125561   .0329665    12.51   0.000      .347943    .4771691
------------------------------------------------------------------------------

.         est store Ineffective

. 
.         coefplot (Effective, mcol(green) label(Highly Effective)) (Avg_Effective, mcol(black) label(Averag
> e)) (Ineffective, mcol(red)),  ylabel(`=1' "Control" `=2' "Treated") title("CCES, Intention to Reelect Law
> maker", size(medium)) legend(row(1) order(6 4 2))

.                 graph save "MainTreatment_CCES_vote.gph", replace
(file MainTreatment_CCES_vote.gph not found)
file MainTreatment_CCES_vote.gph saved

. 
. ** Heterogeneous treatment effects by copartisanship
.         logit voteforrep copartisan##efinfo if effective==1  

Iteration 0:   log likelihood = -140.60687  
Iteration 1:   log likelihood = -87.998038  
Iteration 2:   log likelihood = -87.553506  
Iteration 3:   log likelihood = -87.551271  
Iteration 4:   log likelihood = -87.551271  

Logistic regression                                     Number of obs =    204
                                                        LR chi2(3)    = 106.11
                                                        Prob > chi2   = 0.0000
Log likelihood = -87.551271                             Pseudo R2     = 0.3773

-----------------------------------------------------------------------------------
       voteforrep | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
------------------+----------------------------------------------------------------
     1.copartisan |   3.007661    .564543     5.33   0.000     1.901177    4.114145
         1.efinfo |  -.5696661   .7695504    -0.74   0.459    -2.077957    .9386249
                  |
copartisan#efinfo |
             1 1  |   1.189541   .8933966     1.33   0.183     -.561484    2.940566
                  |
            _cons |   -1.88707    .479899    -3.93   0.000    -2.827654   -.9464849
-----------------------------------------------------------------------------------

.                 margins, at(efinfo=(0(1)1) copartisan=(0(1)1)) post

Adjusted predictions                                       Number of obs = 204
Model VCE: OIM

Expression: Pr(voteforrep), predict()
1._at: copartisan = 0
       efinfo     = 0
2._at: copartisan = 0
       efinfo     = 1
3._at: copartisan = 1
       efinfo     = 0
4._at: copartisan = 1
       efinfo     = 1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .1315789   .0548361     2.40   0.016     .0241022    .2390557
          2  |   .0789474   .0437441     1.80   0.071    -.0067894    .1646842
          3  |   .7540984   .0551353    13.68   0.000     .6460351    .8621616
          4  |   .8507463   .0435337    19.54   0.000     .7654219    .9360707
------------------------------------------------------------------------------

.         est store Effective

. 
.         logit voteforrep copartisan##efinfo if avgeffective==1 

Iteration 0:   log likelihood =  -194.6302  
Iteration 1:   log likelihood = -133.74661  
Iteration 2:   log likelihood = -133.54806  
Iteration 3:   log likelihood = -133.54764  
Iteration 4:   log likelihood = -133.54764  

Logistic regression                                     Number of obs =    281
                                                        LR chi2(3)    = 122.17
                                                        Prob > chi2   = 0.0000
Log likelihood = -133.54764                             Pseudo R2     = 0.3138

-----------------------------------------------------------------------------------
       voteforrep | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
------------------+----------------------------------------------------------------
     1.copartisan |   2.926739   .4359809     6.71   0.000     2.072232    3.781246
         1.efinfo |  -.0264333   .5143892    -0.05   0.959    -1.034618    .9817511
                  |
copartisan#efinfo |
             1 1  |   .2545643   .6436178     0.40   0.692    -1.006903    1.516032
                  |
            _cons |  -1.722767   .3433033    -5.02   0.000    -2.395629   -1.049905
-----------------------------------------------------------------------------------

.                 margins, at(efinfo=(0(1)1) copartisan=(0(1)1)) post

Adjusted predictions                                       Number of obs = 281
Model VCE: OIM

Expression: Pr(voteforrep), predict()
1._at: copartisan = 0
       efinfo     = 0
2._at: copartisan = 0
       efinfo     = 1
3._at: copartisan = 1
       efinfo     = 0
4._at: copartisan = 1
       efinfo     = 1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .1515152   .0441345     3.43   0.001     .0650131    .2380172
          2  |   .1481481    .048343     3.06   0.002     .0533977    .2428986
          3  |   .7692308   .0477057    16.12   0.000     .6757294    .8627322
          4  |   .8072289   .0432992    18.64   0.000      .722364    .8920939
------------------------------------------------------------------------------

.         est store Avg_Effective

. 
.         logit voteforrep copartisan##efinfo if ineffective==1  

Iteration 0:   log likelihood = -250.21367  
Iteration 1:   log likelihood = -174.99927  
Iteration 2:   log likelihood = -173.77641  
Iteration 3:   log likelihood = -173.77127  
Iteration 4:   log likelihood = -173.77127  

Logistic regression                                     Number of obs =    361
                                                        LR chi2(3)    = 152.88
                                                        Prob > chi2   = 0.0000
Log likelihood = -173.77127                             Pseudo R2     = 0.3055

-----------------------------------------------------------------------------------
       voteforrep | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
------------------+----------------------------------------------------------------
     1.copartisan |   3.461287   .4444812     7.79   0.000      2.59012    4.332454
         1.efinfo |  -.3522204   .5989408    -0.59   0.556    -1.526123    .8216821
                  |
copartisan#efinfo |
             1 1  |  -.4151402   .6729026    -0.62   0.537    -1.734005    .9037246
                  |
            _cons |  -2.063693   .3753305    -5.50   0.000    -2.799328   -1.328059
-----------------------------------------------------------------------------------

.                 margins, at(efinfo=(0(1)1) copartisan=(0(1)1)) post

Adjusted predictions                                       Number of obs = 361
Model VCE: OIM

Expression: Pr(voteforrep), predict()
1._at: copartisan = 0
       efinfo     = 0
2._at: copartisan = 0
       efinfo     = 1
3._at: copartisan = 1
       efinfo     = 0
4._at: copartisan = 1
       efinfo     = 1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .1126761   .0375256     3.00   0.003     .0391272    .1862249
          2  |   .0819672   .0351224     2.33   0.020     .0131286    .1508059
          3  |   .8018018   .0378375    21.19   0.000     .7276418    .8759618
          4  |   .6525424   .0438343    14.89   0.000     .5666287    .7384561
------------------------------------------------------------------------------

.         est store Ineffective

. 
.         logit voteforrep efinfo if copartisan==1 & ineffective==1

Iteration 0:   log likelihood = -134.71488  
Iteration 1:   log likelihood = -131.50193  
Iteration 2:   log likelihood =  -131.4776  
Iteration 3:   log likelihood = -131.47759  

Logistic regression                                     Number of obs =    229
                                                        LR chi2(1)    =   6.47
                                                        Prob > chi2   = 0.0109
Log likelihood = -131.47759                             Pseudo R2     = 0.0240

------------------------------------------------------------------------------
  voteforrep | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |  -.7673605   .3067047    -2.50   0.012    -1.368491   -.1662304
       _cons |   1.397594   .2380977     5.87   0.000      .930931    1.864257
------------------------------------------------------------------------------

.         ttest voteforrep if copartisan==1 & ineffective==1, by(efinfo)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. err.   Std. dev.   [95% conf. interval]
---------+--------------------------------------------------------------------
       0 |     111    .8018018    .0380091    .4004502    .7264768    .8771268
       1 |     118    .6525424    .0440212    .4781932    .5653606    .7397241
---------+--------------------------------------------------------------------
Combined |     229    .7248908    .0295748    .4475475     .666616    .7831656
---------+--------------------------------------------------------------------
    diff |            .1492594    .0584741                .0340379    .2644809
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   2.5526
H0: diff = 0                                     Degrees of freedom =      227

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.9943         Pr(|T| > |t|) = 0.0113          Pr(T > t) = 0.0057

.         
.         coefplot (Effective, mcol(green) label(Highly Effective)) (Avg_Effective, mcol(black) label(Averag
> e)) (Ineffective, mcol(red)),  ylabel(`=1' "Outparty, Control" `=2' "Outparty, Treated" `=3' "Copartisan, 
> Control" `=4' "Copartisan, Treated") title("CCES Reelection, by Partisan Alignment", size(medium)) legend(
> row(1) order(6 4 2))

.                 graph save "Partisanship_CCES_vote.gph", replace
(file Partisanship_CCES_vote.gph not found)
file Partisanship_CCES_vote.gph saved

. 
. ** Heterogenenous treatment effects by ideology (for Figure 6 in Paper)
. 
.         logit voteforrep extreme##efinfo if effective==1 

Iteration 0:   log likelihood = -173.21479  
Iteration 1:   log likelihood = -162.79731  
Iteration 2:   log likelihood = -162.77633  
Iteration 3:   log likelihood = -162.77632  

Logistic regression                                     Number of obs =    250
                                                        LR chi2(3)    =  20.88
                                                        Prob > chi2   = 0.0001
Log likelihood = -162.77632                             Pseudo R2     = 0.0603

--------------------------------------------------------------------------------
    voteforrep | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
---------------+----------------------------------------------------------------
     1.extreme |    1.39591   .3848076     3.63   0.000     .6417007    2.150119
      1.efinfo |   .5520685   .3763451     1.47   0.142    -.1855544    1.289691
               |
extreme#efinfo |
          1 1  |   -.526093   .5296688    -0.99   0.321    -1.564225    .5120387
               |
         _cons |  -.8938178   .2797907    -3.19   0.001    -1.442197   -.3454381
--------------------------------------------------------------------------------

.                 margins, at(efinfo=(0(1)1) extreme=(0(1)1)) post

Adjusted predictions                                       Number of obs = 250
Model VCE: OIM

Expression: Pr(voteforrep), predict()
1._at: extreme = 0
       efinfo  = 0
2._at: extreme = 0
       efinfo  = 1
3._at: extreme = 1
       efinfo  = 0
4._at: extreme = 1
       efinfo  = 1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .2903226   .0576468     5.04   0.000      .177337    .4033082
          2  |   .4153846   .0611229     6.80   0.000      .295586    .5351832
          3  |   .6229508   .0620527    10.04   0.000     .5013297     .744572
          4  |   .6290323   .0613492    10.25   0.000     .5087901    .7492744
------------------------------------------------------------------------------

.                 est store Effective

.         
.         logit voteforrep extreme##efinfo if avgeffective==1

Iteration 0:   log likelihood = -254.46761  
Iteration 1:   log likelihood = -246.02499  
Iteration 2:   log likelihood = -246.00977  
Iteration 3:   log likelihood = -246.00977  

Logistic regression                                     Number of obs =    369
                                                        LR chi2(3)    =  16.92
                                                        Prob > chi2   = 0.0007
Log likelihood = -246.00977                             Pseudo R2     = 0.0332

--------------------------------------------------------------------------------
    voteforrep | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
---------------+----------------------------------------------------------------
     1.extreme |  -.0475126   .2978998    -0.16   0.873    -.6313855    .5363604
      1.efinfo |  -.7192194   .2962102    -2.43   0.015    -1.299781   -.1386581
               |
extreme#efinfo |
          1 1  |   1.280767   .4292119     2.98   0.003     .4395268    2.122007
               |
         _cons |  -.0990909   .1992517    -0.50   0.619    -.4896171    .2914353
--------------------------------------------------------------------------------

.                 margins, at(efinfo=(0(1)1) extreme=(0(1)1)) post

Adjusted predictions                                       Number of obs = 369
Model VCE: OIM

Expression: Pr(voteforrep), predict()
1._at: extreme = 0
       efinfo  = 0
2._at: extreme = 0
       efinfo  = 1
3._at: extreme = 1
       efinfo  = 0
4._at: extreme = 1
       efinfo  = 1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .4752475   .0496909     9.56   0.000     .3778552    .5726398
          2  |   .3061225    .046556     6.58   0.000     .2148743    .3973706
          3  |   .4634146   .0550678     8.42   0.000     .3554838    .5713454
          4  |   .6022727   .0521733    11.54   0.000      .500015    .7045304
------------------------------------------------------------------------------

.                 est store Avg_Effective

. 
.         logit voteforrep extreme##efinfo if ineffective==1 

Iteration 0:   log likelihood = -299.92255  
Iteration 1:   log likelihood = -289.37466  
Iteration 2:   log likelihood = -289.34935  
Iteration 3:   log likelihood = -289.34935  

Logistic regression                                     Number of obs =    437
                                                        LR chi2(3)    =  21.15
                                                        Prob > chi2   = 0.0001
Log likelihood = -289.34935                             Pseudo R2     = 0.0353

--------------------------------------------------------------------------------
    voteforrep | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
---------------+----------------------------------------------------------------
     1.extreme |   .5351766   .2765783     1.93   0.053    -.0069069     1.07726
      1.efinfo |  -.4978384   .2833279    -1.76   0.079    -1.053151    .0574741
               |
extreme#efinfo |
          1 1  |   .5665365   .3951135     1.43   0.152    -.2078718    1.340945
               |
         _cons |  -.3894648   .1998463    -1.95   0.051    -.7811564    .0022268
--------------------------------------------------------------------------------

.                 margins, at(efinfo=(0(1)1) extreme=(0(1)1)) post

Adjusted predictions                                       Number of obs = 437
Model VCE: OIM

Expression: Pr(voteforrep), predict()
1._at: extreme = 0
       efinfo  = 0
2._at: extreme = 0
       efinfo  = 1
3._at: extreme = 1
       efinfo  = 0
4._at: extreme = 1
       efinfo  = 1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .4038462   .0481139     8.39   0.000     .3095447    .4981476
          2  |   .2916667   .0414927     7.03   0.000     .2103425    .3729909
          3  |   .5363636   .0475469    11.28   0.000     .4431735    .6295538
          4  |   .5533981   .0489847    11.30   0.000     .4573898    .6494063
------------------------------------------------------------------------------

.                 est store Ineffective

. 
.         logit voteforrep efinfo if extreme==0 & ineffective==1

Iteration 0:   log likelihood = -144.14211  
Iteration 1:   log likelihood = -142.59093  
Iteration 2:   log likelihood = -142.58867  
Iteration 3:   log likelihood = -142.58867  

Logistic regression                                     Number of obs =    224
                                                        LR chi2(1)    =   3.11
                                                        Prob > chi2   = 0.0780
Log likelihood = -142.58867                             Pseudo R2     = 0.0108

------------------------------------------------------------------------------
  voteforrep | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |  -.4978384   .2833279    -1.76   0.079    -1.053151     .057474
       _cons |  -.3894648   .1998463    -1.95   0.051    -.7811564    .0022268
------------------------------------------------------------------------------

.         ttest voteforrep if extreme==0 & ineffective==1, by(efinfo)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. err.   Std. dev.   [95% conf. interval]
---------+--------------------------------------------------------------------
       0 |     104    .4038462    .0483469    .4930435    .3079615    .4997308
       1 |     120    .2916667    .0416667    .4564355    .2091625    .3741708
---------+--------------------------------------------------------------------
Combined |     224      .34375    .0318056    .4760226     .281072     .406428
---------+--------------------------------------------------------------------
    diff |            .1121795    .0634726               -.0129064    .2372654
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   1.7674
H0: diff = 0                                     Degrees of freedom =      222

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.9607         Pr(|T| > |t|) = 0.0785          Pr(T > t) = 0.0393

.         
.         logit voteforrep efinfo if extreme==0 & effective==1

Iteration 0:   log likelihood =  -82.56097  
Iteration 1:   log likelihood = -81.472429  
Iteration 2:   log likelihood = -81.470599  
Iteration 3:   log likelihood = -81.470599  

Logistic regression                                     Number of obs =    127
                                                        LR chi2(1)    =   2.18
                                                        Prob > chi2   = 0.1397
Log likelihood = -81.470599                             Pseudo R2     = 0.0132

------------------------------------------------------------------------------
  voteforrep | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |   .5520686   .3763451     1.47   0.142    -.1855543    1.289691
       _cons |  -.8938179   .2797907    -3.19   0.001    -1.442198   -.3454382
------------------------------------------------------------------------------

.         ttest voteforrep if extreme==0 & effective==1, by(efinfo)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. err.   Std. dev.   [95% conf. interval]
---------+--------------------------------------------------------------------
       0 |      62    .2903226    .0581174    .4576167    .1741097    .4065355
       1 |      65    .4153846    .0615985    .4966232    .2923274    .5384418
---------+--------------------------------------------------------------------
Combined |     127    .3543307    .0426112    .4802043    .2700043    .4386571
---------+--------------------------------------------------------------------
    diff |            -.125062    .0848524               -.2929956    .0428715
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -1.4739
H0: diff = 0                                     Degrees of freedom =      125

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0715         Pr(|T| > |t|) = 0.1430          Pr(T > t) = 0.9285

.         
.         coefplot (Effective, mcol(green) label (Highly Effective)) (Avg_Effective, mcol(black) label (Aver
> age)) (Ineffective, mcol(red)),  ylabel(`=1' "Moderate, Control" `=2' "Moderate, Treated" `=3' "Extreme, C
> ontrol" `=4' "Extreme, Treated")  title("CCES Reelection, by Ideological Extremism") legend(row(1) order(6
>  4 2))

.                 graph save "IdeologicalExtremism_CCES_vote.gph", replace
(file IdeologicalExtremism_CCES_vote.gph not found)
file IdeologicalExtremism_CCES_vote.gph saved

. 
. *Note: Moderates seem more responsive to treatment      
. 
. ************************
. ************************        
. **      Appendix      **
. **       Tables       **
. ************************
. ************************
. 
. ** Appendix table A3
. reg approve efinfo if effective==1

      Source |       SS           df       MS      Number of obs   =       249
-------------+----------------------------------   F(1, 247)       =      2.52
       Model |  .533753706         1  .533753706   Prob > F        =    0.1135
    Residual |  52.2694591       247  .211617244   R-squared       =    0.0101
-------------+----------------------------------   Adj R-squared   =    0.0061
       Total |  52.8032129       248  .212916181   Root MSE        =    .46002

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |   .0926165   .0583168     1.59   0.114    -.0222451    .2074781
       _cons |    .647541   .0416481    15.55   0.000     .5655102    .7295718
------------------------------------------------------------------------------

. reg approve efinfo if avgeffective==1

      Source |       SS           df       MS      Number of obs   =       366
-------------+----------------------------------   F(1, 364)       =      0.03
       Model |  .008252541         1  .008252541   Prob > F        =    0.8518
    Residual |  85.9589606       364  .236150991   R-squared       =    0.0001
-------------+----------------------------------   Adj R-squared   =   -0.0027
       Total |  85.9672131       365  .235526611   Root MSE        =    .48595

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |  -.0094982   .0508092    -0.19   0.852    -.1094147    .0904183
       _cons |   .6277778   .0362208    17.33   0.000     .5565494    .6990062
------------------------------------------------------------------------------

. reg approve efinfo if ineffective==1

      Source |       SS           df       MS      Number of obs   =       434
-------------+----------------------------------   F(1, 432)       =     37.03
       Model |  8.56628317         1  8.56628317   Prob > F        =    0.0000
    Residual |  99.9245002       432  .231306714   R-squared       =    0.0790
-------------+----------------------------------   Adj R-squared   =    0.0768
       Total |  108.490783       433  .250556082   Root MSE        =    .48094

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |  -.2810316   .0461799    -6.09   0.000    -.3717968   -.1902663
       _cons |   .6384977   .0329537    19.38   0.000     .5737281    .7032672
------------------------------------------------------------------------------

. 
. ** Appendix table A4
. logit approve efinfo if effective==1

Iteration 0:   log likelihood = -153.19059  
Iteration 1:   log likelihood = -151.93216  
Iteration 2:   log likelihood = -151.92988  
Iteration 3:   log likelihood = -151.92988  

Logistic regression                                     Number of obs =    249
                                                        LR chi2(1)    =   2.52
                                                        Prob > chi2   = 0.1123
Log likelihood = -151.92988                             Pseudo R2     = 0.0082

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |   .4385395    .277228     1.58   0.114    -.1048174    .9818964
       _cons |   .6082477     .18951     3.21   0.001      .236815    .9796805
------------------------------------------------------------------------------

. logit approve efinfo if avgeffective==1

Iteration 0:   log likelihood = -242.51199  
Iteration 1:   log likelihood = -242.49442  
Iteration 2:   log likelihood = -242.49442  

Logistic regression                                     Number of obs =    366
                                                        LR chi2(1)    =   0.04
                                                        Prob > chi2   = 0.8513
Log likelihood = -242.49442                             Pseudo R2     = 0.0001

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |  -.0404429   .2157663    -0.19   0.851    -.4633371    .3824512
       _cons |   .5226952   .1541912     3.39   0.001      .220486    .8249044
------------------------------------------------------------------------------

. logit approve efinfo if ineffective==1

Iteration 0:   log likelihood = -300.80744  
Iteration 1:   log likelihood = -283.45335  
Iteration 2:   log likelihood = -283.44128  
Iteration 3:   log likelihood = -283.44128  

Logistic regression                                     Number of obs =    434
                                                        LR chi2(1)    =  34.73
                                                        Prob > chi2   = 0.0000
Log likelihood = -283.44128                             Pseudo R2     = 0.0577

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |  -1.155229   .2001011    -5.77   0.000     -1.54742   -.7630378
       _cons |   .5688495   .1426182     3.99   0.000     .2893229     .848376
------------------------------------------------------------------------------

. 
. ** Appendix table A7
. reg approve efinfo if effective==1  & copartisan==1

      Source |       SS           df       MS      Number of obs   =       128
-------------+----------------------------------   F(1, 126)       =      1.74
       Model |  .169753181         1  .169753181   Prob > F        =    0.1896
    Residual |  12.2989968       126  .097611086   R-squared       =    0.0136
-------------+----------------------------------   Adj R-squared   =    0.0058
       Total |    12.46875       127  .098179134   Root MSE        =    .31243

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |   .0729141   .0552907     1.32   0.190    -.0365046    .1823328
       _cons |    .852459   .0400023    21.31   0.000     .7732957    .9316223
------------------------------------------------------------------------------

. reg approve efinfo if effective==1  & copartisan==0

      Source |       SS           df       MS      Number of obs   =        76
-------------+----------------------------------   F(1, 74)        =      0.23
       Model |  .052631579         1  .052631579   Prob > F        =    0.6341
    Residual |  17.0526316        74  .230440967   R-squared       =    0.0031
-------------+----------------------------------   Adj R-squared   =   -0.0104
       Total |  17.1052632        75  .228070175   Root MSE        =    .48004

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |   .0526316   .1101293     0.48   0.634    -.1668059    .2720691
       _cons |   .3157895   .0778732     4.06   0.000     .1606237    .4709552
------------------------------------------------------------------------------

. reg approve efinfo if avgeffective==1  & copartisan==1

      Source |       SS           df       MS      Number of obs   =       161
-------------+----------------------------------   F(1, 159)       =      2.84
       Model |  .332837322         1  .332837322   Prob > F        =    0.0941
    Residual |  18.6609515       159  .117364475   R-squared       =    0.0175
-------------+----------------------------------   Adj R-squared   =    0.0113
       Total |  18.9937888       160   .11871118   Root MSE        =    .34258

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |  -.0909793    .054025    -1.68   0.094    -.1976785    .0157199
       _cons |   .9102564   .0387901    23.47   0.000     .8336461    .9868667
------------------------------------------------------------------------------

. reg approve efinfo if avgeffective==1  & copartisan==0

      Source |       SS           df       MS      Number of obs   =       117
-------------+----------------------------------   F(1, 115)       =      1.46
       Model |  .342083842         1  .342083842   Prob > F        =    0.2286
    Residual |  26.8544974       115  .233517368   R-squared       =    0.0126
-------------+----------------------------------   Adj R-squared   =    0.0040
       Total |  27.1965812       116  .234453286   Root MSE        =    .48324

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |   .1084656    .089616     1.21   0.229    -.0690464    .2859776
       _cons |   .3174603   .0608821     5.21   0.000     .1968647     .438056
------------------------------------------------------------------------------

. reg approve efinfo if ineffective==1  & copartisan==1

      Source |       SS           df       MS      Number of obs   =       229
-------------+----------------------------------   F(1, 227)       =     52.95
       Model |  9.19271018         1  9.19271018   Prob > F        =    0.0000
    Residual |  39.4099099       227  .173611938   R-squared       =    0.1891
-------------+----------------------------------   Adj R-squared   =    0.1856
       Total |  48.6026201       228  .213169386   Root MSE        =    .41667

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |  -.4009009   .0550941    -7.28   0.000    -.5094621   -.2923397
       _cons |   .9009009   .0395483    22.78   0.000     .8229721    .9788297
------------------------------------------------------------------------------

. reg approve efinfo if ineffective==1  & copartisan==0

      Source |       SS           df       MS      Number of obs   =       130
-------------+----------------------------------   F(1, 128)       =      8.59
       Model |   1.4507326         1   1.4507326   Prob > F        =    0.0040
    Residual |  21.6261905       128  .168954613   R-squared       =    0.0629
-------------+----------------------------------   Adj R-squared   =    0.0555
       Total |  23.0769231       129  .178890877   Root MSE        =    .41104

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |  -.2119048   .0723156    -2.93   0.004    -.3549936    -.068816
       _cons |   .3285714   .0491288     6.69   0.000     .2313617    .4257811
------------------------------------------------------------------------------

. 
. ** Appendix table A8
. logit approve copartisan##efinfo if effective==1  

Iteration 0:   log likelihood = -126.89802  
Iteration 1:   log likelihood = -93.467784  
Iteration 2:   log likelihood = -92.032415  
Iteration 3:   log likelihood = -92.015568  
Iteration 4:   log likelihood = -92.015559  

Logistic regression                                     Number of obs =    204
                                                        LR chi2(3)    =  69.76
                                                        Prob > chi2   = 0.0000
Log likelihood = -92.015559                             Pseudo R2     = 0.2749

-----------------------------------------------------------------------------------
          approve | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
------------------+----------------------------------------------------------------
     1.copartisan |   2.527209   .5021322     5.03   0.000     1.543048     3.51137
         1.efinfo |   .2341934   .4846546     0.48   0.629    -.7157122    1.184099
                  |
copartisan#efinfo |
             1 1  |   .5294824   .7624702     0.69   0.487    -.9649317    2.023896
                  |
            _cons |  -.7731899   .3489912    -2.22   0.027      -1.4572   -.0891797
-----------------------------------------------------------------------------------

. logit approve copartisan##efinfo if avgeffective==1  

Iteration 0:   log likelihood = -179.17203  
Iteration 1:   log likelihood = -140.29768  
Iteration 2:   log likelihood = -138.98266  
Iteration 3:   log likelihood = -138.97455  
Iteration 4:   log likelihood = -138.97455  

Logistic regression                                     Number of obs =    278
                                                        LR chi2(3)    =  80.39
                                                        Prob > chi2   = 0.0000
Log likelihood = -138.97455                             Pseudo R2     = 0.2244

-----------------------------------------------------------------------------------
          approve | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
------------------+----------------------------------------------------------------
     1.copartisan |   3.082237    .479789     6.42   0.000     2.141868    4.022606
         1.efinfo |   .4669749    .385995     1.21   0.226    -.2895614    1.223511
                  |
copartisan#efinfo |
             1 1  |  -1.272287   .6223394    -2.04   0.041     -2.49205   -.0525241
                  |
            _cons |  -.7654678   .2706581    -2.83   0.005    -1.295948   -.2349877
-----------------------------------------------------------------------------------

. logit approve copartisan##efinfo if ineffective==1  

Iteration 0:   log likelihood = -248.33682  
Iteration 1:   log likelihood = -184.21904  
Iteration 2:   log likelihood = -183.59267  
Iteration 3:   log likelihood = -183.59055  
Iteration 4:   log likelihood = -183.59055  

Logistic regression                                     Number of obs =    359
                                                        LR chi2(3)    = 129.49
                                                        Prob > chi2   = 0.0000
Log likelihood = -183.59055                             Pseudo R2     = 0.2607

-----------------------------------------------------------------------------------
          approve | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
------------------+----------------------------------------------------------------
     1.copartisan |   2.921928   .4070184     7.18   0.000     2.124187     3.71967
         1.efinfo |  -1.309728   .4758991    -2.75   0.006    -2.242473   -.3769833
                  |
copartisan#efinfo |
             1 1  |  -.8975465   .6010718    -1.49   0.135    -2.075626    .2805326
                  |
            _cons |  -.7146534   .2544698    -2.81   0.005    -1.213405   -.2159018
-----------------------------------------------------------------------------------

.         
. ** Appendix table A11
. reg approve efinfo if extreme==1 & effective==1

      Source |       SS           df       MS      Number of obs   =       123
-------------+----------------------------------   F(1, 121)       =      0.13
       Model |  .024611661         1  .024611661   Prob > F        =    0.7233
    Residual |  23.6501851       121  .195456075   R-squared       =    0.0010
-------------+----------------------------------   Adj R-squared   =   -0.0072
       Total |  23.6747967       122  .194055711   Root MSE        =     .4421

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |  -.0282919    .079729    -0.35   0.723    -.1861365    .1295527
       _cons |   .7540984   .0566056    13.32   0.000     .6420326    .8661641
------------------------------------------------------------------------------

. reg approve efinfo if extreme==0 & effective==1

      Source |       SS           df       MS      Number of obs   =       126
-------------+----------------------------------   F(1, 124)       =      6.50
       Model |  1.42584119         1  1.42584119   Prob > F        =    0.0120
    Residual |  27.2090794       124   .21942806   R-squared       =    0.0498
-------------+----------------------------------   Adj R-squared   =    0.0421
       Total |  28.6349206       125  .229079365   Root MSE        =    .46843

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |   .2128625   .0835045     2.55   0.012     .0475838    .3781413
       _cons |   .5409836   .0599765     9.02   0.000     .4222733    .6596939
------------------------------------------------------------------------------

. reg approve efinfo if extreme==1 & avgeffective==1

      Source |       SS           df       MS      Number of obs   =       170
-------------+----------------------------------   F(1, 168)       =      0.09
       Model |  .020868658         1  .020868658   Prob > F        =    0.7641
    Residual |  38.8026608       168  .230968219   R-squared       =    0.0005
-------------+----------------------------------   Adj R-squared   =   -0.0054
       Total |  38.8235294       169  .229725026   Root MSE        =    .48059

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |  -.0221729   .0737653    -0.30   0.764    -.1677994    .1234535
       _cons |   .6585366   .0530725    12.41   0.000     .5537617    .7633114
------------------------------------------------------------------------------

. reg approve efinfo if extreme==0 & avgeffective==1

      Source |       SS           df       MS      Number of obs   =       196
-------------+----------------------------------   F(1, 194)       =      0.00
       Model |  7.1054e-15         1  7.1054e-15   Prob > F        =    1.0000
    Residual |  46.9591837       194  .242057648   R-squared       =    0.0000
-------------+----------------------------------   Adj R-squared   =   -0.0052
       Total |  46.9591837       195  .240816327   Root MSE        =    .49199

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |          0   .0702848     0.00   1.000    -.1386204    .1386204
       _cons |   .6020408   .0496989    12.11   0.000     .5040214    .7000603
------------------------------------------------------------------------------

. reg approve efinfo if extreme==1 & ineffective==1

      Source |       SS           df       MS      Number of obs   =       213
-------------+----------------------------------   F(1, 211)       =     11.57
       Model |   2.7347567         1   2.7347567   Prob > F        =    0.0008
    Residual |  49.8943513       211   .23646612   R-squared       =    0.0520
-------------+----------------------------------   Adj R-squared   =    0.0475
       Total |   52.629108       212  .248250509   Root MSE        =    .48628

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |  -.2267432   .0666744    -3.40   0.001    -.3581765   -.0953098
       _cons |   .6636364   .0463648    14.31   0.000     .5722389    .7550339
------------------------------------------------------------------------------

. reg approve efinfo if extreme==0 & ineffective==1

      Source |       SS           df       MS      Number of obs   =       221
-------------+----------------------------------   F(1, 219)       =     25.90
       Model |  5.75593012         1  5.75593012   Prob > F        =    0.0000
    Residual |  48.6694092       219  .222234745   R-squared       =    0.1058
-------------+----------------------------------   Adj R-squared   =    0.1017
       Total |  54.4253394       220  .247387906   Root MSE        =    .47142

------------------------------------------------------------------------------
     approve | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |  -.3235149   .0635686    -5.09   0.000    -.4487994   -.1982304
       _cons |   .6116505   .0464502    13.17   0.000     .5201039    .7031971
------------------------------------------------------------------------------

. 
. ** Appendix table A12
. logit approve extreme##efinfo if effective==1  

Iteration 0:   log likelihood = -153.19059  
Iteration 1:   log likelihood = -148.83239  
Iteration 2:   log likelihood = -148.79433  
Iteration 3:   log likelihood = -148.79433  

Logistic regression                                     Number of obs =    249
                                                        LR chi2(3)    =   8.79
                                                        Prob > chi2   = 0.0322
Log likelihood = -148.79433                             Pseudo R2     = 0.0287

--------------------------------------------------------------------------------
       approve | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
---------------+----------------------------------------------------------------
     1.extreme |   .9562881   .3929671     2.43   0.015     .1860869    1.726489
      1.efinfo |   .9549285   .3859086     2.47   0.013     .1985615    1.711296
               |
extreme#efinfo |
          1 1  |  -1.102071   .5642491    -1.95   0.051    -2.207978    .0038373
               |
         _cons |   .1643031   .2569384     0.64   0.523    -.3392869     .667893
--------------------------------------------------------------------------------

. logit approve extreme##efinfo if avgeffective==1  

Iteration 0:   log likelihood = -242.51199  
Iteration 1:   log likelihood = -242.07302  
Iteration 2:   log likelihood = -242.07291  
Iteration 3:   log likelihood = -242.07291  

Logistic regression                                     Number of obs =    366
                                                        LR chi2(3)    =   0.88
                                                        Prob > chi2   = 0.8307
Log likelihood = -242.07291                             Pseudo R2     = 0.0018

--------------------------------------------------------------------------------
       approve | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
---------------+----------------------------------------------------------------
     1.extreme |   .2428037   .3111639     0.78   0.435    -.3670663    .8526738
      1.efinfo |   2.57e-15   .2918567     0.00   1.000    -.5720287    .5720287
               |
extreme#efinfo |
          1 1  |  -.0971637   .4341892    -0.22   0.823     -.948159    .7538315
               |
         _cons |   .4139758   .2063739     2.01   0.045     .0094904    .8184612
--------------------------------------------------------------------------------

. logit approve extreme##efinfo if ineffective==1  

Iteration 0:   log likelihood = -300.80744  
Iteration 1:   log likelihood = -280.52517  
Iteration 2:   log likelihood = -280.47726  
Iteration 3:   log likelihood = -280.47725  

Logistic regression                                     Number of obs =    434
                                                        LR chi2(3)    =  40.66
                                                        Prob > chi2   = 0.0000
Log likelihood = -280.47725                             Pseudo R2     = 0.0676

--------------------------------------------------------------------------------
       approve | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
---------------+----------------------------------------------------------------
     1.extreme |   .2252863   .2856548     0.79   0.430    -.3345869    .7851594
      1.efinfo |  -1.358711   .2866872    -4.74   0.000    -1.920608   -.7968149
               |
extreme#efinfo |
          1 1  |   .4253894   .4029625     1.06   0.291    -.3644026    1.215181
               |
         _cons |   .4542553   .2021708     2.25   0.025     .0580079    .8505027
--------------------------------------------------------------------------------

. 
. ** Appendix table A14
. reg approve4 efinfo if effective==1

      Source |       SS           df       MS      Number of obs   =       249
-------------+----------------------------------   F(1, 247)       =      3.75
       Model |  .301494233         1  .301494233   Prob > F        =    0.0539
    Residual |  19.8481451       247  .080356863   R-squared       =    0.0150
-------------+----------------------------------   Adj R-squared   =    0.0110
       Total |  20.1496393       248  .081248546   Root MSE        =    .28347

------------------------------------------------------------------------------
    approve4 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |   .0696077    .035936     1.94   0.054    -.0011723    .1403877
       _cons |   .5651639   .0256644    22.02   0.000     .5146149     .615713
------------------------------------------------------------------------------

. reg approve4 efinfo if avgeffective==1

      Source |       SS           df       MS      Number of obs   =       366
-------------+----------------------------------   F(1, 364)       =      0.07
       Model |  .004618261         1  .004618261   Prob > F        =    0.7913
    Residual |  23.9614434       364  .065828141   R-squared       =    0.0002
-------------+----------------------------------   Adj R-squared   =   -0.0026
       Total |  23.9660616       365  .065660443   Root MSE        =    .25657

------------------------------------------------------------------------------
    approve4 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |  -.0071054   .0268259    -0.26   0.791    -.0598585    .0456477
       _cons |   .5514333   .0191236    28.84   0.000     .5138267    .5890399
------------------------------------------------------------------------------

. reg approve4 efinfo if ineffective==1

      Source |       SS           df       MS      Number of obs   =       434
-------------+----------------------------------   F(1, 432)       =     34.77
       Model |  2.71608409         1  2.71608409   Prob > F        =    0.0000
    Residual |  33.7462215       432  .078116253   R-squared       =    0.0745
-------------+----------------------------------   Adj R-squared   =    0.0723
       Total |  36.4623056       433  .084208558   Root MSE        =    .27949

------------------------------------------------------------------------------
    approve4 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |  -.1582452   .0268367    -5.90   0.000     -.210992   -.1054984
       _cons |   .5410235   .0191505    28.25   0.000     .5033837    .5786633
------------------------------------------------------------------------------

. 
. ** Appendix table A16
. reg approve4 efinfo if effective==1  & copartisan==1

      Source |       SS           df       MS      Number of obs   =       128
-------------+----------------------------------   F(1, 126)       =      2.25
       Model |  .119914733         1  .119914733   Prob > F        =    0.1357
    Residual |   6.7012522       126  .053184541   R-squared       =    0.0176
-------------+----------------------------------   Adj R-squared   =    0.0098
       Total |  6.82116693       127  .053709976   Root MSE        =    .23062

------------------------------------------------------------------------------
    approve4 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |   .0612828   .0408127     1.50   0.136    -.0194843      .14205
       _cons |   .7045082   .0295276    23.86   0.000      .646074    .7629424
------------------------------------------------------------------------------

. reg approve4 efinfo if effective==1  & copartisan==0

      Source |       SS           df       MS      Number of obs   =        76
-------------+----------------------------------   F(1, 74)        =      0.55
       Model |  .036564329         1  .036564329   Prob > F        =    0.4612
    Residual |  4.93107475        74  .066636145   R-squared       =    0.0074
-------------+----------------------------------   Adj R-squared   =   -0.0061
       Total |  4.96763908        75  .066235188   Root MSE        =    .25814

------------------------------------------------------------------------------
    approve4 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |   .0438684   .0592213     0.74   0.461    -.0741326    .1618695
       _cons |   .3680526   .0418758     8.79   0.000     .2846133     .451492
------------------------------------------------------------------------------

. reg approve4 efinfo if avgeffective==1  & copartisan==1

      Source |       SS           df       MS      Number of obs   =       161
-------------+----------------------------------   F(1, 159)       =      4.42
       Model |  .158903971         1  .158903971   Prob > F        =    0.0371
    Residual |  5.71908616       159  .035969095   R-squared       =    0.0270
-------------+----------------------------------   Adj R-squared   =    0.0209
       Total |  5.87799013       160  .036737438   Root MSE        =    .18966

------------------------------------------------------------------------------
    approve4 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |  -.0628628   .0299083    -2.10   0.037    -.1219316   -.0037941
       _cons |   .7089231   .0214742    33.01   0.000     .6665116    .7513346
------------------------------------------------------------------------------

. reg approve4 efinfo if avgeffective==1  & copartisan==0

      Source |       SS           df       MS      Number of obs   =       117
-------------+----------------------------------   F(1, 115)       =      1.07
       Model |  .078472077         1  .078472077   Prob > F        =    0.3033
    Residual |  8.44146682       115  .073404059   R-squared       =    0.0092
-------------+----------------------------------   Adj R-squared   =    0.0006
       Total |   8.5199389       116  .073447749   Root MSE        =    .27093

------------------------------------------------------------------------------
    approve4 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |   .0519497   .0502442     1.03   0.303    -.0475743    .1514738
       _cons |   .3859206   .0341342    11.31   0.000     .3183074    .4535339
------------------------------------------------------------------------------

. reg approve4 efinfo if ineffective==1  & copartisan==1

      Source |       SS           df       MS      Number of obs   =       229
-------------+----------------------------------   F(1, 227)       =     43.60
       Model |  2.32272399         1  2.32272399   Prob > F        =    0.0000
    Residual |  12.0932102       227  .053274054   R-squared       =    0.1611
-------------+----------------------------------   Adj R-squared   =    0.1574
       Total |  14.4159342       228  .063227781   Root MSE        =    .23081

------------------------------------------------------------------------------
    approve4 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |  -.2015181   .0305192    -6.60   0.000    -.2616552   -.1413809
       _cons |   .6841622   .0219077    31.23   0.000     .6409938    .7273306
------------------------------------------------------------------------------

. reg approve4 efinfo if ineffective==1  & copartisan==0

      Source |       SS           df       MS      Number of obs   =       130
-------------+----------------------------------   F(1, 128)       =     13.25
       Model |  .887259802         1  .887259802   Prob > F        =    0.0004
    Residual |  8.57238959       128  .066971794   R-squared       =    0.0938
-------------+----------------------------------   Adj R-squared   =    0.0867
       Total |  9.45964939       129  .073330615   Root MSE        =    .25879

------------------------------------------------------------------------------
    approve4 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |   -.165719   .0455295    -3.64   0.000     -.255807   -.0756311
       _cons |   .3710857   .0309312    12.00   0.000      .309883    .4322884
------------------------------------------------------------------------------

.  
.  ** Appendix table A18
. reg approve4 efinfo if extreme==1 & effective==1

      Source |       SS           df       MS      Number of obs   =       123
-------------+----------------------------------   F(1, 121)       =      0.01
       Model |  .000859699         1  .000859699   Prob > F        =    0.9185
    Residual |  9.88473365       121  .081692014   R-squared       =    0.0001
-------------+----------------------------------   Adj R-squared   =   -0.0082
       Total |  9.88559335       122  .081029454   Root MSE        =    .28582

------------------------------------------------------------------------------
    approve4 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |  -.0052877   .0515444    -0.10   0.918    -.1073334    .0967581
       _cons |   .6608361   .0365953    18.06   0.000     .5883861     .733286
------------------------------------------------------------------------------

. reg approve4 efinfo if extreme==0 & effective==1

      Source |       SS           df       MS      Number of obs   =       126
-------------+----------------------------------   F(1, 124)       =      9.39
       Model |  .665843263         1  .665843263   Prob > F        =    0.0027
    Residual |   8.7944342       124  .070922856   R-squared       =    0.0704
-------------+----------------------------------   Adj R-squared   =    0.0629
       Total |  9.46027746       125   .07568222   Root MSE        =    .26631

------------------------------------------------------------------------------
    approve4 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |    .145462   .0474741     3.06   0.003     .0514975    .2394266
       _cons |   .4694918   .0340979    13.77   0.000     .4020024    .5369812
------------------------------------------------------------------------------

. reg approve4 efinfo if extreme==1 & avgeffective==1

      Source |       SS           df       MS      Number of obs   =       170
-------------+----------------------------------   F(1, 168)       =      0.36
       Model |   .02495924         1   .02495924   Prob > F        =    0.5474
    Residual |  11.5362159       168  .068667952   R-squared       =    0.0022
-------------+----------------------------------   Adj R-squared   =   -0.0038
       Total |  11.5611752       169   .06840932   Root MSE        =    .26205

------------------------------------------------------------------------------
    approve4 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |  -.0242489    .040221    -0.60   0.547    -.1036527    .0551549
       _cons |   .5768171   .0289381    19.93   0.000     .5196879    .6339463
------------------------------------------------------------------------------

. reg approve4 efinfo if extreme==0 & avgeffective==1

      Source |       SS           df       MS      Number of obs   =       196
-------------+----------------------------------   F(1, 194)       =      0.04
       Model |   .00222245         1   .00222245   Prob > F        =    0.8518
    Residual |  12.3168419       194  .063488876   R-squared       =    0.0002
-------------+----------------------------------   Adj R-squared   =   -0.0050
       Total |  12.3190644       195  .063174689   Root MSE        =    .25197

------------------------------------------------------------------------------
    approve4 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |   .0067347   .0359957     0.19   0.852    -.0642585    .0777279
       _cons |   .5301939   .0254528    20.83   0.000     .4799941    .5803936
------------------------------------------------------------------------------

. reg approve4 efinfo if extreme==1 & ineffective==1

      Source |       SS           df       MS      Number of obs   =       213
-------------+----------------------------------   F(1, 211)       =      9.16
       Model |  .735214295         1  .735214295   Prob > F        =    0.0028
    Residual |  16.9427797       211  .080297534   R-squared       =    0.0416
-------------+----------------------------------   Adj R-squared   =    0.0370
       Total |   17.677994       212  .083386764   Root MSE        =    .28337

------------------------------------------------------------------------------
    approve4 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |   -.117566   .0388531    -3.03   0.003     -.194156    -.040976
       _cons |      .5541   .0270181    20.51   0.000       .50084      .60736
------------------------------------------------------------------------------

. reg approve4 efinfo if extreme==0 & ineffective==1

      Source |       SS           df       MS      Number of obs   =       221
-------------+----------------------------------   F(1, 219)       =     27.17
       Model |  2.01054262         1  2.01054262   Prob > F        =    0.0000
    Residual |  16.2071065       219  .074005052   R-squared       =    0.1104
-------------+----------------------------------   Adj R-squared   =    0.1063
       Total |  18.2176491       220  .082807496   Root MSE        =    .27204

------------------------------------------------------------------------------
    approve4 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      efinfo |  -.1912023   .0366832    -5.21   0.000    -.2634996    -.118905
       _cons |   .5270583   .0268048    19.66   0.000     .4742299    .5798866
------------------------------------------------------------------------------

.  
. 
end of do-file

. do "/Users/daniel.butler/Dropbox/BHVW_Replication/Code/CombineFiguresReplication.do"

. * This do-file combines the graphs from the MTurk and CCES studies into single Figures for Paper
. cd "~/Dropbox/BHVW_Replication/Code/Results/"
/Users/daniel.butler/Dropbox/BHVW_Replication/Code/Results

. 
. *For Figure 1:
. graph combine knowledge_mturk.gph knowledge_cces.gph, col(1) iscale(1) title("Figure 1:" "Knowledge about 
> Lawmaker Effectiveness (Control Group)", size(large)) graphregion(fcolor(white) lcolor(white) ifcolor(whit
> e) ilcolor(white))

.         graph save Figure1.gph, replace
(file Figure1.gph not found)
file Figure1.gph saved

.         graph export "Figure1.png", replace
file /Users/daniel.butler/Dropbox/BHVW_Replication/Code/Results/Figure1.png saved as PNG format

.         
. *For Figure 4:
. graph combine MainTreatment_Mturk.gph MainTreatment_CCES.gph, col(1) iscale(1) title("", size(large)) grap
> hregion(fcolor(white) lcolor(white) ifcolor(white) ilcolor(white))

.         graph save Figure4.gph, replace
(file Figure4.gph not found)
file Figure4.gph saved

.         graph export "Figure4.png", replace
file /Users/daniel.butler/Dropbox/BHVW_Replication/Code/Results/Figure4.png saved as PNG format

. 
. *For Figure 5:
. graph combine Partisanship_Mturk.gph Partisanship_CCES.gph, col(1) title("", size(large)) graphregion(fcol
> or(white) lcolor(white) ifcolor(white) ilcolor(white))

.         graph save Figure5.gph, replace
(file Figure5.gph not found)
file Figure5.gph saved

.         graph export "Figure5.png", replace
file /Users/daniel.butler/Dropbox/BHVW_Replication/Code/Results/Figure5.png saved as PNG format

. 
. *For Figure 6:
. graph combine IdeologicalExtremism_Mturk.gph IdeologicalExtremism_CCES.gph, col(1) title("", size(medlarge
> )) graphregion(fcolor(white) lcolor(white) ifcolor(white) ilcolor(white))

.         graph save Figure6.gph, replace
(file Figure6.gph not found)
file Figure6.gph saved

.         graph export "Figure6.png", replace
file /Users/daniel.butler/Dropbox/BHVW_Replication/Code/Results/Figure6.png saved as PNG format

. 
.         
. *For Figure A1:
. graph combine MainTreatment_Mturk_Appendix.gph MainTreatment_CCES_Appendix.gph, col(1) iscale(1) title("Fi
> gure A1: Informational Effects on Approval of Lawmaker", size(large)) graphregion(fcolor(white) lcolor(whi
> te) ifcolor(white) ilcolor(white))

.         graph save FigureA1.gph, replace
(file FigureA1.gph not found)
file FigureA1.gph saved

.         graph export "FigureA1.png", replace
file /Users/daniel.butler/Dropbox/BHVW_Replication/Code/Results/FigureA1.png saved as PNG format

. 
. *For Figure A2:
. graph combine Partisanship_Mturk_Appendix.gph Partisanship_CCES_Appendix.gph, col(1) title("Figure A2: Tre
> atment Effects on Approval, by Partisanship", size(large)) graphregion(fcolor(white) lcolor(white) ifcolor
> (white) ilcolor(white))

.         graph save FigureA2.gph, replace
(file FigureA2.gph not found)
file FigureA2.gph saved

.         graph export "FigureA2.png", replace
file /Users/daniel.butler/Dropbox/BHVW_Replication/Code/Results/FigureA2.png saved as PNG format

. 
. *For Figure A3:
. graph combine IdeologicalExtremism_Mturk_Appendix.gph IdeologicalExtremism_CCES_Appendix.gph, col(1) title
> ("Figure A3: Treatment Effects on Approval, by Ideological Extremism", size(medlarge)) graphregion(fcolor(
> white) lcolor(white) ifcolor(white) ilcolor(white))

.         graph save FigureA3.gph, replace
(file FigureA3.gph not found)
file FigureA3.gph saved

.         graph export "FigureA3.png", replace
file /Users/daniel.butler/Dropbox/BHVW_Replication/Code/Results/FigureA3.png saved as PNG format

.         
. *For Figure A4:
. graph combine MainTreatment_Mturk_vote.gph MainTreatment_CCES_vote.gph, col(1) iscale(1) title("Figure A4:
>  Informational Effects on Vote Intention", size(large)) graphregion(fcolor(white) lcolor(white) ifcolor(wh
> ite) ilcolor(white))

.         graph save FigureA4.gph, replace
(file FigureA4.gph not found)
file FigureA4.gph saved

.         graph export "FigureA4.png", replace
file /Users/daniel.butler/Dropbox/BHVW_Replication/Code/Results/FigureA4.png saved as PNG format

. 
. *For Figure A5:
. graph combine Partisanship_Mturk_vote.gph Partisanship_CCES_vote.gph, col(1) title("Figure A5: Treatment E
> ffects on Vote Intention, by Partisanship", size(large)) graphregion(fcolor(white) lcolor(white) ifcolor(w
> hite) ilcolor(white))

.         graph save FigureA5.gph, replace
(file FigureA5.gph not found)
file FigureA5.gph saved

.         graph export "FigureA5.png", replace
file /Users/daniel.butler/Dropbox/BHVW_Replication/Code/Results/FigureA5.png saved as PNG format

. 
. *For Figure A6:
. graph combine IdeologicalExtremism_Mturk_vote.gph IdeologicalExtremism_CCES_vote.gph, col(1) title("Figure
>  A6: Treatment Effects on Vote Intention, by Ideological Extremism", size(medlarge)) graphregion(fcolor(wh
> ite) lcolor(white) ifcolor(white) ilcolor(white))

.         graph save FigureA6.gph, replace
(file FigureA6.gph not found)
file FigureA6.gph saved

.         graph export "FigureA6.png", replace
file /Users/daniel.butler/Dropbox/BHVW_Replication/Code/Results/FigureA6.png saved as PNG format

. 
. 
end of do-file

. log close
      name:  <unnamed>
       log:  /Users/daniel.butler/Dropbox/My Mac (1064-AL-05001.lan)/Desktop/BHVW Results.log
  log type:  text
 closed on:   9 Sep 2021, 09:27:17
------------------------------------------------------------------------------------------------------------
